Coding for compressive imaging

ABSTRACT

Embodiments of the present invention can estimate an image using an encodement provided by an optical component, an encodement provided by a detector array, or both an encodement provided by an optical component and an encodement provided by a detector array. The original image can be encoded with a dispersed point spread function created by the optical component. The detector array can be encoded with a pixel sampling function. The encoded image is measured using the detector array. An estimated image can be calculated from the pixel sampling function and the measured image, the dispersed point spread function and the measured image, or the dispersed point spread function, the pixel sampling function, and the measured image. The number of pixels in the estimated image is greater than the number of measurements in the measured image.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part application of U.S. patent application Ser. No. 11/183,838, filed Jul. 19, 2005, which claims the benefit of U.S. Provisional Patent Application Ser. No. 60/589,475 filed Jul. 20, 2004, U.S. Provisional Patent Application Ser. No. 60/589,530 filed Jul. 20, 2004, and U.S. Provisional Patent Application Ser. No. 60/622,181 filed Oct. 26, 2004. This application also claims the benefit of U.S. Provisional Patent Application No. 60/836,375 filed Aug. 9, 2006. All of the above mentioned applications are incorporated by reference herein in their entireties.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments of the present invention relate to systems and methods for compressive sampling in spectroscopy and spectral imaging. More particularly, embodiments of the present invention relate to systems and methods for using less than one measurement per estimated signal value in spectroscopy and spectral imaging.

2. Background Information

A signal is a physical phenomenon distributed over a space. Examples include signals distributed over time, such as electromagnetic waves on antennas or transmission lines, signals distributed over Fourier space, such as optical or electrical spectra, and multidimensional signals distributed over physical space, such as 2D and 3D images. Digital signal analysis consists of signal estimation from discrete measurements. For many years, sampling theory formed the theoretical core of signal analysis. Basic signal characterization required regular sampling at a rate proportional to the signal bandwidth. The minimal sampling rate is termed the Nyquist frequency.

Over the past couple of decades, however, novel sampling approaches have emerged. One generalized sampling approach envisioned a bank of filtered copies of a signal at sub-Nyquist rates. Since this generalized sampling approach, a number of generalized sampling strategies and analyses have emerged. Particular attention has been given sampling on multiresolution bases and to irregular sampling. Multiresolution sampling is regarded as a generalization of generalized sampling and is used as a means of balancing sampling rates and parallel signal analysis.

Signal compression technology has developed in parallel with generalized sampling theory. Multiresolution representations, implemented using fractal and wavelet analysis, have been found to be critically enabling for signal compression on the basis of the empirically observed self-similarity of natural signals on multiple scales. For appropriate bases, natural signals yield sparse multiscale representations. Sparsity and hierarchical self-similarity have been combined in signal compression algorithms such as the embedded zero-tree and set partitioning in hierarchical trees algorithms.

Generalized sampling and compression technologies resolve specific challenges in signal processing systems. Generalized sampling enables systems to sample at lower rates than the Nyquist frequency, and data compression enables post-sampling reduction of the system data load.

Previous examples of sub-Nyquist sampling are divided into parameter estimation approaches and interpolation approaches. Parameter estimation uses sampled data to fit an a priori signal model. The signal model typically involves much greater constraints than conventional band limits. For example, one can assume that the signal consists of a single point source or a source state described by just a few parameters. As an example of parameter estimation, several studies have considered estimation of the frequency or position of an unknown but narrow-band source from sub-Nyquist samples. Interpolation generates new samples from measured data by curve fitting between known points.

In view of the foregoing, it can be appreciated that a substantial need exists for systems and methods that can advantageously provide for sub-Nyquist sampling that can be used in spectroscopy and spectral imaging.

BRIEF SUMMARY OF THE INVENTION

One embodiment of the present invention is a method for estimating an image using an encodement provided by an optical component. The image is encoded with a dispersed point spread function created by the optical component. The encoded image is measured using a detector array. An estimated image is calculated from the dispersed point spread function and the measured image using a processor.

Another embodiment of the present invention is a method for estimating an image using an encodement provided by a detector array. The detector array is encoded with a pixel sampling function. The image is measured using the detector array. An estimated image is calculated from the pixel sampling function and the measured image using a processor.

Another embodiment of the present invention is a method for estimating an image using an encodement provided by an optical component and an encodement provided by a detector array. The image is encoded with a dispersed point spread function created by the optical component. The detector array is encoded with a pixel sampling function. The encoded image is measured using the detector array. An estimated image is calculated from the dispersed point spread function, the pixel sampling function, and the measured image.

Another embodiment of the present invention is a focal plane system for estimating an image using an encodement provided by an optical component. The system includes the optical component, a detector array, and a processor. The optical component creates a dispersed point spread function and encodes the image with the dispersed point spread function. The detector array measures the encoded image. The processor calculates an estimated image from the dispersed point spread function and the measured image.

Another embodiment of the present invention is a focal plane system for estimating an image using an encodement provided by a detector array. The system includes the detector array and a processor. The detector array is encoded with a pixel sampling function and measures the image. The processor calculates an estimated image from the pixel sampling function and the measured image.

Another embodiment of the present invention is a focal plane system for estimating an image using an encodement provided by an optical component and an encodement provided by a detector array. The system includes the optical component, the detector array, and a processor. The optical component creates a dispersed point spread function and encodes the image with the dispersed point spread function. The detector array is encoded with a pixel sampling function and measures the encoded image. The processor calculates an estimated image from the dispersed point spread function, the pixel sampling function, and the measured image.

In various embodiments of the present invention, the number of pixels in the estimated image is greater than the number of measurements in the measured image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary plot of a planar function space where a subspace spanned by the physical sampling is isomorphic to a subspace spanned by possible signals, in accordance with an embodiment of the present invention.

FIG. 2 is an exemplary plot of a planar function space where a subspace spanned by the physical sampling is indicative of a subspace spanned by possible signals, in accordance with an embodiment of the present invention.

FIG. 3 is a flowchart showing an exemplary method for compressively sampling an optical signal using a spectrometer, in accordance with an embodiment of the present invention.

FIG. 4 is a schematic diagram of an exemplary spectrometer implementing arbitrary mapping, in accordance with an embodiment of the present invention.

FIG. 5 is a plot of an exemplary spectral transmission mask for multiscale Haar sampling, in accordance with an embodiment of the present invention.

FIG. 6 is an exemplary 8×8 transformation code for a quantized cosine transform, in accordance with an embodiment of the present invention.

FIG. 7 is a comparison of exemplary simulated image reconstructions using permuted Hadamard transform, discrete cosine transform, and quantized cosine transform matrices using 4.69%, 15.63%, and 32.81% of the transformed components or available measurements, in accordance with an embodiment of the present invention.

FIG. 8 is a schematic diagram of an exemplary spectrometer employing compressive sampling, in accordance with an embodiment of the present invention.

FIG. 9 is a plot of an exemplary spectral transmission mask used to make measurements on Haar-wavelet averages and details, in accordance with an embodiment of the present invention.

FIG. 10 is a flowchart showing an exemplary method for reconstructing Haar-wavelet averages and details for the exemplary spectral transmission mask shown in FIG. 9, in accordance with an embodiment of the present invention.

FIG. 11 is a plot of an exemplary comparison of original neon spectral data and reconstructed neon spectral data using the exemplary method shown in FIG. 10, in accordance with an embodiment of the present invention.

FIG. 12 is a plot of an exemplary comparison of original neon wavelet coefficient data and reconstructed neon coefficient data using the exemplary method shown in FIG. 10, in accordance with an embodiment of the present invention.

FIG. 13 is a schematic diagram showing an exemplary optical wavemeter, in accordance with an embodiment of the present invention.

FIG. 14 is a plot of an exemplary mask for a 256-channel wavemeter with binary sensors, in accordance with an embodiment of the present invention.

FIG. 15 is a plot of an exemplary mask for a 256-channel wavemeter with 4-level sensors, in accordance with an embodiment of the present invention.

FIG. 16 is a depiction of an exemplary sample image taken using the exemplary optical wavemeter of FIG. 13 and the exemplary mask of FIG. 14, in accordance with an embodiment of the present invention.

FIG. 17 is a plot of exemplary expected bit patterns for each of 100 wavelengths using the exemplary optical wavemeter of FIG. 13 and the exemplary mask of FIG. 14, in accordance with an embodiment of the present invention.

FIG. 18 is a plot of exemplary measured bit patterns for each of 100 wavelengths using the exemplary optical wavemeter of FIG. 13 and the exemplary mask of FIG. 14, in accordance with an embodiment of the present invention.

FIG. 19 is a plot of exemplary measured errors in bit patterns for each of 100 wavelengths using the exemplary optical wavemeter of FIG. 13 and the exemplary mask of FIG. 14, in accordance with an embodiment of the present invention.

FIG. 20 is a plot of exemplary quaternary digit patterns for each of 100 wavelengths using the exemplary optical wavemeter of FIG. 13 and the exemplary mask of FIG. 15, in accordance with an embodiment of the present invention.

FIG. 21 is a plot of exemplary measured quaternary digit patterns for each of 100 wavelengths using the exemplary optical wavemeter of FIG. 13 and the exemplary mask of FIG. 15, in accordance with an embodiment of the present invention.

FIG. 22 is a plot of exemplary measured errors in quaternary digit patterns for each of 100 wavelengths using the exemplary optical wavemeter of FIG. 13 and the exemplary mask of FIG. 15, in accordance with an embodiment of the present invention.

FIG. 23 is a flowchart showing an exemplary method for temporally compressively sampling a signal, in accordance with an embodiment of the present invention.

FIG. 24 is a flowchart showing an exemplary method for spatially focal plane coding an optical signal using an imaging system, in accordance with an embodiment of the present invention.

FIG. 25 is a flowchart showing an exemplary method for temporally focal plane coding an optical signal using an imaging system, in accordance with an embodiment of the present invention.

FIG. 26 is a flowchart showing an exemplary method for spatially and temporally focal plane coding an optical signal using an imaging system, in accordance with an embodiment of the present invention.

FIG. 27 is a flowchart showing an exemplary method for compressively sampling an optical signal using multiplex sampling, in accordance with an embodiment of the present invention.

FIG. 28 is a flowchart showing an exemplary method for compressively sampling an optical signal using a reflective mask, in accordance with an embodiment of the present invention.

FIG. 29 is a flowchart showing an exemplary method for compressively sampling an optical signal using an optical component to encode multiplex measurements, in accordance with an embodiment of the present invention.

FIG. 30 is a schematic diagram showing a focal plane system for estimating an image, in accordance with an embodiment of the present invention.

FIG. 31 is a schematic diagram showing a focal plane system for estimating an image that includes an optical component, in accordance with an embodiment of the present invention.

FIG. 32 is a flowchart showing a method for estimating an image using an encodement provided by an optical component, in accordance with an embodiment of the present invention.

FIG. 33 is a flowchart showing a method for estimating an image using an encodement provided by a detector array, in accordance with an embodiment of the present invention.

FIG. 34 is a flowchart showing a method for estimating an image using an encodement provided by an optical component and an encodement provided by a detector array, in accordance with an embodiment of the present invention.

FIG. 35 is 90 by 140 portion of an exemplary 500 by 500 image of a random point cloud, in accordance with an embodiment of the present invention.

FIG. 36 is an exemplary 32 by 32 dispersed point spread function created by an optical component of a focal plane imager, in accordance with an embodiment of the present invention.

FIG. 37 is an exemplary 120 by 120 measured image of the 500 by 500 point cloud, partially shown in FIG. 35, using a focal plane imager, in accordance with an embodiment of the present invention.

FIG. 38 is a 90×140 portion of an exemplary estimated image of the 500 by 500 point cloud, partially shown in FIG. 35, using a focal plane imager, in accordance with an embodiment of the present invention.

Before one or more embodiments of the invention are described in detail, one skilled in the art will appreciate that the invention is not limited in its application to the details of construction, the arrangements of components, and the arrangement of steps set forth in the following detailed description or illustrated in the drawings. The invention is capable of other embodiments and of being practiced or being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

DETAILED DESCRIPTION OF THE INVENTION

The goal of compressive sampling is to reduce the number of measurements needed to characterize a signal. The defining characteristic of compressive sampling is that less than one measurement is registered in the system per signal value estimated. Compressive sampling can be viewed as an implementation of data compression in the physical layer.

One embodiment of the present invention is a method for using compressive sampling in optical imaging and spectroscopy. Positivity is the defining feature of images and spectra. The nominal detected signal is envisioned as a positive count of the number of photons detected over a certain spatial and temporal window. It is, of course, possible to introduce negative and complex weights in optical measurements using spatial, temporal or spectral filters. For optical imaging, compressive sampling enables cameras with more pixels in the estimated image than in the electronic focal plane. Compressive sampling can be particularly important in spectral and temporal sampling of image data, in which cases the number of measurements per spatial resolution element can or cannot exceed one but the number of measurements per estimated sample in the full space-time-spectral data cube is much less than one. For optical spectroscopy, compressive sampling enables spectral characterization with fewer detectors than conventional systems. Deeper spectroscopic significance arises, however, from using compressive sampling for direct molecular or atomic analysis without forming a conventional spectrum.

Linearity and predeterminism are the defining features of sampling strategies. Linearity refers to the condition that measurements, g, consist of linear projections of a source state f. Formally the measurement process is expressed as g=Hf+b, where b represents noise. Predeterminism refers to the condition that the measurement transformation H is fixed prior to measurement and is independent of the outcome of any single measurement. While there are many examples of adaptive measurement in both imaging and spectroscopy, simplicity dictates focusing on single step non-adaptive measurement.

Compressive sampling integrates previous work in signal sampling, representation and compression, multiplex sensing and group testing. Compressive sampling is a novel approach for sub-Nyquist frequency measurement. One novelty is in the combination of generalized sampling and multiscale data compression strategies with two previously unrelated approaches: multiplex sensing and group testing. Multiplex sensing consists of taking measurements that depend on multiple source points in the object space under examination.

Multiplex sensing involves the concept of the object space. The object space terminology originates in spectroscopy, where Fourier and Hadamard transform instruments are referred to as multiplex spectrometers in contrast to isomorphic instruments based on grating diffraction. In optical systems, multiplex sensing has also been applied in coded aperture and interferometric imaging systems. A variety of other sensor modalities are viewed as multiplex systems, including computed tomography and coherence tomography sensors.

Conventionally, the attractions of multiplex sensing have included increased throughput (e.g., better light detection efficiency) and improved signal verses noise discrimination via weighing design. In tomographic systems, the additional advantage of native multidimensional image reconstruction arises. Recently reference structure tomography has been explored as a new approach to multiplex sensing and it has been shown that the additional advantage of compressive sampling can be obtained in multiplex systems.

Previously, it was shown that the number of measurements needed to characterize a sample can be reduced by adaptive multiscale sensing. This approach combined conventional measurements with prior knowledge of the expected multiscale structure of the object under analysis. While quite effective in guiding magnetic resonance image acquisition, this approach requires data be acquired in serial and that current and future measurements depend on past measurements. These constraints are unattractive in real-time spectroscopy and imaging.

Compressive sampling extends the adaptive multiscale sensing approach by using multiplex sensing to implement group testing on multiscale features. Group testing has generally been applied to analysis of discrete objects, such as coins, weights or chemical samples. Another embodiment of the present invention is a method that applies group sampling to filter bank samples drawn from spatially distributed signals. In this embodiment, group testing consists of making signal measurements which depend on disjoint groups of resolution or filter elements abstracted from the signal.

1. Sampling in Signal Representation and Measurement

Representation sampling is distinct from measurement sampling. Representation sampling refers to the mathematical representation of a continuous function ƒ(x), xε

^(d), in terms of finite or countably many discrete numbers. A representation sampling system consists of a rule for generating sample values from ƒ(x) and a rule for approximating ƒ(x) from the sample values. As an example, the sampling rule for Nyquist-Shannon sampling returns values of ƒ(x) at equally spaced points and signal approximation consists of summing products of sample values with the sinc basis. Measurement sampling refers to the physical process of measuring “samples” of ƒ(x). For example, a camera measures samples of the light intensity on a focal plane. The measurement samples are the raw signal values measured from the physical pixels.

Imagers and spectrometers infer representation samples from measurement samples of physical signals. In many cases, as in a simple camera, it is assumed that representation samples are identical to measurement samples. Of course, representation samples are not unique for a given signal. Sensor systems are often modeled by continuous transformations of ƒ(x), as in the Fourier or Radon transform, prior to discrete sampling. In these cases, representations are constructed using discrete bases on the transformed space, and measurement samples are treated as representation samples on the transform space. This approach is taken, for example, in x-ray tomography. Representation samples can also take many forms on the native continuous space of ƒ(x). In most cases it is assumed that this space is L²(

^(d)). Many bases spanning subspaces of L²(

^(d)) have been considered over the past twenty years in the context of wavelet and generalized sampling theories. Compression and multiscale signal fidelity have been primary motivators in considering new bases, which have not generally been closely associated with measurement sampling.

Measurement sampling on transformed spaces are selected on the basis of physical characteristics of measurement systems. Tomographic systems, for example, measure Radon transforms rather than native samples because Radon data can be measured non-invasively. Generalized representation sampling, in contrast, has been considered primarily in post-processing. It is possible to design sensors for which measurements directly estimate generalized representation samples, but to date examples of this approach are rare. In optical systems, physical constraints, in particular, require that optical signals be non-negative, and modal coherence constraints limit the capacity to implement arbitrary sampling schemes.

Isomorphism between representation samples and measurement samples is not necessarily a design goal. In Radon and Fourier transform sensor systems, the fact that measurement is not local on the native signal space is generally treated as an annoyance. Generalized representation sampling focuses primarily on high fidelity description of the signal on the native space. In contrast, the goal of compressive sampling is to ensure that measured data is as useful as possible in estimating the signal. Given a finite number of measurements, surprisingly little analysis of whether or not conventional samples represent a good strategy has been done. Issues of uniform verses nonuniform sampling have been widely considered, but such analyses normally consider the sample itself to relatively simple and local in a continuous space.

A precise model for what is meant by a “measurement” and how the measurement is associated with the signal is needed. The process of measurement is associated with the process of analog to digital conversion, and measurement is specifically defined to be the process of abstracting a digital number from an analog process. A measurement sample is thus a digital number with at least partial dependence on an analog state.

While representation samples are in theory continuous or discrete, in practice representation sample values are also rational numbers. The primary difference between measurement samples and representation samples is that representation samples are associated in some simple way with signal values in a continuous space. Measurement samples are, in principle, associated with any property of the signal and need not be locally dependent on the signal value.

Optical sensing includes a long history of sensing based on non-local measurements, dating back to before the Michelson interferometer. Concepts of locality are also fluid in optics. For example, Fourier space is the native signal space for spectroscopy. Over the past half century, Fourier and Hadamard spectrometers have been particularly popular non-local measurement systems. Sensors for which measurements depend on multiple points in the native signal space are called multiplex sensors in the optical literature.

Embodiments of the present invention examine and exploit deliberately encoded differences between representation samples and measurement samples. In particular, these embodiments show that the number of measurements needed to characterize a signal can be less than the number of representation samples used to describe the signal. A system in which less than one measurement is made per reconstructed representation sample is compressively sampled. Multiplex sensing is essential to compressive sampling.

The process of transforming measurement samples to the representation space is referred to as signal inference. In this section, basic relationships between representation samples and measurement samples are established. The process of physical layer signal compression as implemented by multiplex measurements is described and characterized. Linear and nonlinear decompression strategies are detailed. Finally, algorithms, designs, and performance of compressive sampling systems are considered in succeeding sections.

Representation sampling is described by sampling functions ψ_(i)(x) and reconstruction functions φ_(i)(x) such that the signal ƒ, ƒεL²(

^(d)), is represented as $\begin{matrix} {{{f(x)} = {\sum\limits_{i}{f_{i}{\phi_{i}(x)}}}},} & (1) \end{matrix}$ where ƒ_(i)=

ψ_(i),ƒ

=∫ƒ(x)ψ_(i)(x)dx.  (2)

In Whittaker-Shannon sampling, for example, φ_(i)(x)=sinc(π(x−i)) and ψ_(i)(x)=δ(x−i), assuming the sampling period is scaled to one. A representation sampling model is a mapping between a sample space of discrete vectors and a reconstruction space of continuous functions. The function space associated with Shannon's sampling, for example, consists of band-limited functions in L²(

), any function ƒ(x) in the subspace can be exactly represented, or reconstructed perfectly from the samples, by Eqn. (1).

Discrete representation of a continuous function is the motivation for representation sampling. Ideally, ƒ(x) lies within the subspace spanned by the sampling functions. In practice, the process of projecting ƒ(x) onto the sampling basis and representing on the reconstruction basis is a projection of ƒ(x) onto a subspace of the full range of possible signals.

For simplicity, the case that a model uses the same family of orthonormal functions φ_(i)(x) for both the sampling and the reconstruction functions is considered. Vector φ denotes the function family and V_(c) denotes the associated function space. For any function ƒ(x)εV_(c), its orthogonal projection onto the space spanned by φ_(i)(x) is

φ_(i),ƒ

φ_(i)(x).

φi,ƒ

is referred to as the canonical component or representation of ƒ along the canonical axis φ_(i)(x). The canonical representation of ƒεV_(c) is then the vector $\begin{matrix} {f_{c} = {\int_{x}{{\phi(x)}{f(x)}{{\mathbb{d}x}.}}}} & (3) \end{matrix}$

In general, f_(c) ^(T)φ is the projection of ƒ into the space V_(c). The components of f_(c) are referred to as the canonical signal values. In the case of a digital imaging system, f_(c) is the digital image of the object. In the case of a spectrometer, f_(c) represents the projection of the spectrum of the source under test into the space V_(c).

In contrast to representation sampling, the goal of measurement sampling is to convert the analog realization of a signal into digital form. The measurement samples need not be simply connected to a reconstruction basis and the measurement sampling functions need not be drawn from an orthogonal basis. A linear measurement system transforms ƒ(x) into discrete measurements, denoted by g, as follows $\begin{matrix} {g = {\int_{x}{{h(x)}{f(x)}{\mathbb{d}x}}}} & (4) \end{matrix}$ where h is the vector of measurement response functions h_(n)(x), which is also referred to as the physical sampling functions. In contrast with the sophisticated and complete basis of Shannon sampling, the rectangle averaging function is common in measurement sampling. A digital camera, for example, integrates the value of the optical intensity over a square pixel.

Measurement sampling is connected to representation sampling through the physical sampling functions h_(n)(x). In designing an optical sensor system, h(x) and the representation basis φ_(n)(x) are selected such that h_(n)(x)εV_(c). Then, h=Hψ,  (5) with h _(ij) =∫h _(i)(x)ψ_(j)(x)dx.  (6)

In view of Eqn. (3) and Eqn. (5), Eqn. (4) can be expressed as follows g=Hf _(c) +b  (7) where b represents noise.

The goal of imaging systems and spectrometers is to estimate ƒ(x) via f_(c) from the physical measurements g. Eqn. (7) is the basic model for the forward transformation from ƒ(x) to measurements. Signal estimation consists of inversion of this model. The utility and effectiveness of these systems is evaluated based on measures of the fidelity between the estimated and canonical signals. The measurement system is completely specified by matrix H. It is a multiplex system if H has non-zero off-diagonal elements. A measurement system is compressive if the length of g is less than the length of f_(c) (i.e., the number of rows is less than the number of columns in H).

With compressive sampling, the null space of H is multi-dimensional. V_(h) represents the subspace of V_(c) spanned by the physical sampling functions. V_(⊥) represents the subspace orthogonal to V_(h) in V_(c). Functions in V_(⊥) produce zero measurement by the measurement system H. The canonical representation of function ƒεV_(c) can be decomposed into two components, f_(c)=f_(h)+f_(⊥), representing the components of ƒ in V_(h) and V_(⊥), respectively.

Since f_(⊥) produces zero measurement, Eqn. (7) reduces to g=Hf _(h) +b  (8) Eqn. (8) is generally well-conditioned for estimation of f_(h). But estimation of f_(⊥) from g is impossible in general without further information.

Signal inference is the process of estimating f_(c) from f_(h) with additional knowledge about the signals of interest. V_(ƒ) denotes the subset of V_(c) spanned by possible signals ƒ(x). If V_(h) is isomorphic to V_(ƒ), g approximates f_(c). If V_(h) is indicative of V_(ƒ), there is a one-to-one mapping between g and f_(c).

The distinction between isomorphic and indicative measurement is shown in FIGS. 1 and 2, where V_(c) is a plane. FIG. 1 is an exemplary plot of a planar function space 100, V_(c), where a subspace spanned by the physical sampling 110, V_(h), is isomorphic to subspace spanned by possible signals 120, V_(ƒ), in accordance with an embodiment of the present invention. Isomorphic signal inference is attractive when V_(ƒ) 120 corresponds to a linear subset of V_(c) 100. In FIG. 1, V_(ƒ) 120 corresponds to a line on the plane. V_(h) 110 is made equal to line 120.

In FIG. 1, V_(ƒ) 120 is a straight line, or an affine space in V_(c) 100. A simple linear projection is be used to relate V_(h) 110 to V_(ƒ) 120. The projection completely describes the state of the signal f_(c). The sampling is therefore compressive and the inference is linear.

FIG. 2 is an exemplary plot of a planar function space 200, V_(c), where a subspace spanned by the physical sampling 210, V_(h), is indicative of a subspace spanned by possible signals 220, V_(ƒ), in accordance with an embodiment of the present invention. Indicative signal inference is attractive when V_(ƒ) 220 is indexed by a linear projection of V_(c) 200. In FIG. 2, V_(ƒ) 220 corresponds to a curve on the plane. If V_(h) 210 is a line parallel to the vertical axis, a one-to-one correspondence is drawn between points in V_(h) 210 and points in V_(ƒ) 220. Each point in V_(h) 210 is mapped on to the corresponding point in V_(ƒ) 210 by adding a unique vector drawn from V_(⊥).

In FIG. 2, V_(ƒ) 220 is a curve of one parameter. Its canonical components are dependent on the parameter. The components, by compressive sampling, can be used as an index or as parameters of ƒ_(c). When V_(ƒ) 220 is known a priori, f_(h) uniquely determines the signal f_(c).

The challenge in isomorphic measurement is to discover the subset of V_(c) that approximates V_(ƒ). Discovery is accomplished by Karhunen-Loève decomposition, for example, and similar methods. Indicative measurement poses the dual challenges of characterizing V_(ƒ) and of development of an algorithm to map g on to f_(c). While isomorphic inference is always linear in g, linear and nonlinear methods of indicative inference can be considered.

The primary challenges in using signal inference to achieve compressive sampling are characterization of V_(ƒ), design of H such that V_(h) is both indicative of V_(ƒ) and physically achievable in sensor hardware, and development of an efficient and accurate algorithm mapping g into an estimate for f_(h) and mapping the estimated f_(h) into an estimate for f_(c).

In the case of linear signal inference, V_(ƒ) is typically characterized by analyzing a representative sample of signals methods such as Karhunen-Loève decomposition. Estimation algorithms are relatively straightforward in this case. In nonlinear inference, f_(h) is linearly determined by measurement g, but the relationship between g and f_(⊥) is nonlinear and represented via the integration of the information of f_(h) with prior knowledge regarding V_(ƒ). For example, efficient group testing sensors have been demonstrated for the case in which V_(ƒ) is known to consist of isolated point sources in V_(c). In an extreme case, it is known that f_(c) has at most one nonzero component. Signals of much greater complexity for which an articulated description of V_(ƒ) is unavailable are considered below.

For signal reference, various methods to explore and exploit the relationship between g and ƒ_(⊥) are presented. For example, the components of ƒ_(c) from multi-scale group testing measurement have a hierarchically local relationship. After the estimation of ƒ_(h), methods that attempt to optimize certain objective measures over the null space of H are used, subject to certain constraints. Both the optimization objectives and the constraints are based on the priori knowledge of the targeted signals. The priori knowledge may be described by physical models, statistical models, or their combinations. For example, one set of group testing methods rely on Bayesian inference based on statistical models of spectra and images as well as multiplex measurements.

2. Sampling in Spectroscopy and Imaging

Earlier it was shown that a linear sensor drawing measurement samples from a continuous distribution is characterized by the representation sample to measurement sample mapping g=Hf _(c) +b  (9) Here the physical realization of this mapping in optical spectrometers and imagers is considered.

In some optical systems, such as coherent holography, the optical field is characterized such that g, H and f_(c) are bipolar or complex. In conventional photography and spectroscopy, however, f_(c) represents a nonnegative power spectral density or radiance. It is assumed that the components of g, H and f_(c) are nonnegative and real. The primary motivation for this restriction is the realization that potential bipolar or complex representations not withstanding, the vast majority of images and spectra of interest are represented as nonnegative arrays.

Within the constraint of nonnegativity, the broad classes of transformations H are implemented on spectra and images in optical systems. Many examples of Hadamard and Fourier transform spectroscopy and imaging systems have been implemented. Nonnegative Hadamard and Fourier transforms are implemented in these systems by adding a constant positive bias to the transformation kernel. Hadamard transform spectrometers, for example, sample the S matrix, where if

is a Hadamard matrix S=(1+

)/2. Similarly Fourier transform systems sample (1+

{

})/2 where

is the Fourier transform kernel.

Selection of a physically achievable and numerically attractive kernel H is the core of sensor system design. In some cases, such as 3D imaging, H must have a multiplex structure because identity mappings are not physically achievable. In conventional spectroscopy and imaging, however, identity mapping is physically achievable if aperture size and system volume are not constrained. Despite the fact that the identity mapping is the only mapping with condition number one under the non-negativity constraint, in some cases multiplexing is performed as a means of achieving compressive sampling and of optimizing design constraints such as sensor cost, sensor sampling rate, and design geometry.

FIG. 3 is a flowchart showing an exemplary method 300 for compressively sampling an optical signal, in accordance with an embodiment of the present invention.

In step 310 of method 300, an optical component with a plurality of transmissive elements and a plurality of opaque elements is created. The location of the plurality of transmissive elements and the plurality of opaque elements is determined by a transmission function. The optical component includes but is not limited to a transmission mask or a coded aperture.

In step 320, the spectrum of the optical signal is dispersed across the optical component.

In step 330, signals transmitted by the plurality of transmissive elements are detected in a single time step at each sensor of a plurality of sensors dispersed spatially with respect to the optical component. Each sensor of the plurality of sensors produces a measurement resulting in a number of measurements for the single time step.

In step 340, a number of estimated optical signal values is calculated from the number of measurements and the transmission function. The transmission function is selected so that the number of measurements is less than the number of estimated optical signal values.

In another embodiment of the present invention, the number of estimated optical signal values is calculated by multiplying the number of measurements by a pseudo-inverse of the transmission function. In another embodiment of the present invention, the number of estimated optical signal values is calculated by a linear constrained optimization procedure. In another embodiment of the present invention, the number of estimated optical signal values is calculated by a non-linear constrained optimization procedure. In another embodiment of the present invention, the optical component is adaptively or dynamically variable. In another embodiment of the present invention, the adaptively or dynamically variable optical component is based on electro-optic effects, liquid crystals, or micro mechanical devices. In another embodiment of the present invention, the optical component implements positive and respective negative elements of an encoding.

Arbitrary nonnegative mappings are implemented in spectroscopy. FIG. 4 is a schematic diagram of an exemplary spectrometer 400 implementing arbitrary mapping, in accordance with an embodiment of the present invention. Single mode spectrometer 400 is a baseline design. In spectroscopy of a single spatial mode, the input field is mapped through a pinhole spatial filter 410. Pinhole 410 is collimated by spherical lens 420 and diffracted by grating 430. Grating 430 disperses the spectrum by creating a linear mapping between direction of propagation or wavenumber and wavelength. Cylindrical lens 440, following grating 430, focuses the field along the dispersion direction such that spectral components at coded mask 450 are separated into lines. If each line is a column, codes arranged perpendicular to the dispersion direction in coded mask 450 modulate the spectrum in rows. The ij^(th) pixel on coded mask 450 then corresponds to the ij^(th) component of H, h_(ij). The value of h_(ij) equal to the transmittance of coded mask 450 at the corresponding pixel and is set arbitrarily between zero and one. The power transmitted by each row is focused by cylindrical lens pair 460 on to single output detector 470, such that the set of measurements made on the output detector is g=Hf, where f corresponds to the power spectral density of the input source. f is calculated from g and H using processor 480, for example.

FIG. 4 demonstrates a form of compressive sampling in building an optical wavemeter, in accordance with an embodiment of the present invention. While this approach demonstrates that any filter array can be implemented in a spectrometer, the resulting system is physically nearly as large as a fully sampled spectrometer with the same resolution. Another embodiment of the present invention is a multiplex spectrometer with much smaller size that uses a photonic crystal filter. In this embodiment, which is a compact system, it is not possible to implement arbitrary filter functions. In other embodiments of the present invention, coded mask 450 is implemented using a holographic filter, a holographic filter array, a multiplex grating hologram, a multipass interferometer, a thin film filter, an array of thin film filters, and an array of photonic crystal filters.

Another embodiment of the present invention is a method involving multiscale sampling and group testing of correlated and uncorrelated signal components. Signals are random processes with finite correlation distances. In the simplest model the signal component ƒ_(i) is highly correlated with the signal component ƒ_(i+1), but is independent of ƒ_(i+N) for sufficiently large N. Thus, if a group test is implemented on two uncorrelated coefficients linear combinations of ƒ_(i) and ƒ_(i+N) can be measured. Such delocalized group tests are easily implemented in a long baseline spectrometer system.

FIG. 5 is a plot of an exemplary spectral transmission mask 500 for multiscale Haar sampling, in accordance with an embodiment of the present invention. Transmission mask 500 is, for example, coded mask 450 of spectrometer 400 in FIG. 4. In transmission mask 400, the spectrum consists of 256 channels. The first 16 rows sample the mean spectrum of length 16 blocks. The next 16 rows sample the means of the first eight channels sampled in the previous blocks, the final 128 measurements sample individual channels. Mask 500 represents a unipolar nonnegative sampling of the Haar wavelet decomposition, with nonnegativity obtained by zeroing the negative components as discussed above for Hadamard and Fourier spectroscopy. Mask 500 is not compressive, 256 spectra are reconstructed from 256 measurements.

Arbitrary group testing is more challenging in imaging systems. While global transformations H are available in interferometric imaging systems, it is much easier to implement mappings such that only a compact region of signal elements is active in any row of H, corresponding to measurements that sample only a local neighborhood in the image. Pixel remapping for measurement in imaging systems is achieved through optical elements, such as multiple apertures, beamsplitters, gratings or birefringent elements or by electronic combination of sensor values on the detection plane.

3. Linear Signal Inference

In linear signal inference, the estimated or reconstructed signal, via its canonical representation f_(c), is linearly related to the measurements g. Ideally, the sampling space V_(h) contains the source space V_(s). While digital compression systems may implement essentially any transformation H for any specified source space, the range of physically achievable sensor transformations is more limited. In other words, V_(h) and V_(s) are not the same in many practical situations. The components is estimated in the subspace orthogonal to V_(h), based on both the obtainable solutions and priori knowledge about the joint space of V_(s) and V_(⊥). The priori knowledge is described in two aspects: the physical aspect and the statistical aspect. It can be exploited in signal estimate or reconstruction as well as in the design of sampling system H.

3.1 Compressive Sampling

The primary challenge of compressive sampling by linear inference lies in the joint optimization of the logical suitability and physical plausibility of H. The design of sensing system H includes the selection of representation basis functions ψ and the design of sampling functions. In imaging, it is both desirable and feasible to select representational functions with spatially local support at multiple scales, such as a family of multi-scale wavelet functions. In spectroscopy, a spectral function is expected to be continuous and have one or more than one peak. Representational functions are selected so that both the base and peak features are captured well with as few as possible basis functions. Some representational functions, therefore, have global support.

3.1.1 Quantized Cosine Transform (QCT)

In one embodiment of the present invention, the elements of H are drawn from the set (−1,0,1). This embodiment is implemented using complementary measurements H₁ and H₂ such that H=H₁-H₂ with elements of H₁ and H₂ drawn from the binary set (0,1). Coding schemes based on such matrices can be implemented.

A non-compressive design is viewed as an extreme case where all measurements are used. In a compressive design, certain transforms are used to enable measurements of the principle components of the source image in a representation and to enable source estimates by numerical decompression with high fidelity.

In another embodiment of the present invention an image source is partitioned into blocks of, for example, 8×8 pixels. Consider the double-sided transformation of each 8×8 block S_(ij), C_(ij)=QS_(ij)Q^(†). FIG. 6 is an exemplary 8×8 transformation matrix 600, Q, for a QCT, in accordance with an embodiment of the present invention.

Transform matrix 600 has the following properties. Its elements are from the set (0, 1, −1). The rows of matrix 600 are orthogonal. The row vectors are nearly even in Euclidean length, with the ratio between the largest and the smallest. When the source image is spatially continuous within block S_(ij), the transformed block C_(ij) exhibits the compressible property that its elements decay along the diagonals. The elements on the lower anti-diagonals are, therefore, truncated and the remaining elements are measured with fewer sensors. Denote by C _(ij) the truncated block matrix. An estimate of the source block S_(ij) is then obtained from Q⁻¹ CijQ^(†) (decompression). The same transform matrix is used for all blocks of image S.

The above ideas are similar to the image compression with discrete cosine transforms, as used in the JPEG protocol. In fact, the specific matrix Q can be obtained by rounding the discrete cosine transform (DCT) of the second kind into the set (0, 1, −1). Q is referred to as the quantized cosine transform (QCT). But, the very structure of the QCT matrix itself can be used to explain the compression.

FIG. 7 is a comparison 700 of exemplary simulated image reconstructions using permuted Hadamard transform (PHT), DCT, and QCT matrices using 4.69%, 15.63%, and 32.81% of the transformed components or available measurements, in accordance with an embodiment of the present invention. Visually the effectiveness of the compression with the QCT is surprisingly close to that with the DCT. A PHT matrix with row ordering [1, 5, 7, 3, 4, 8, 2, 6] is also used. Based on the basic 8×8 QCT and PHT matrices, larger transform matrices of hierarchical structure can be constructed for multiple resolution analysis.

3.2 Signal Inference by Deterministic Characteristics

Another embodiment of the present invention is a method for linear signal inference. More specifically, a first order approximation of ƒ_(⊥) from ƒ_(h) is estimated based on priori knowledge of the source space. Priori knowledge about deterministic features of a signal distribution is described, accurately or approximately, by linear operators mapping from ƒ_(h) to ƒ. The following optimization problem for locating the deviation is based on the priori knowledge about the source function $\begin{matrix} {{\min\limits_{f_{n} \in {{null}{(H)}}}{{\nabla\left( {f_{*} + f_{n}} \right)}}_{2}} = {\min\limits_{y}{{\nabla\left( {f_{*} + {Ny}} \right)}}_{2}}} & (10) \end{matrix}$ where ∇ is the backward finite difference, or the discrete gradient, the column of N span the null space of H, and ∥·∥₂ is the Euclidean vector norm. In matrix form, the matrix for the backward finite difference, denoted by B_(∇), is a lower bidiagonal matrix with all the diagonal elements equal to 1 and all the supper-diagonal elements equal to −1. Via the normal equation corresponding to the minimization problem (10), the explicit linear dependence of f_(n) on f_(*) is as follows f _(n) =Ny=N(C ^(T) C)⁻¹ C ^(T) B _(∇)ƒ_(*)  (11) where C=B_(∇)N. In practical computation, the matrix N can be obtained once for all from H such as via the QR factorization. The component f_(n) is computed via a least-squares (LS) solution to (10) instead of using the closed-form expression of (11).

Another embodiment of the present invention is a method of determining a linear inference with minimal gradient. In step one, the input is obtained. The input includes sampling matrix H, measurement vector g. In step two, a projection into the sampling space is calculated. This calculation includes solving for a solution f_(*) to the underdetermined system of equations. In step three, a minimal gradient calculated. This calculation includes obtaining the matrix N and solving f_(n) using the least-squares solution in (10).

3.3 Signal Inference by Statistical Characteristics

Statistical knowledge of a signal distribution is exploited in at least two ways. One is based on the Karhunen-Lo'eve transform, the other is based on conditional probabilities. The statistics are drawn from a sufficiently large data set. The data set is represented with a matrix X. The j-th column corresponds to the samples of the j-th random variable x_(j). S is the matrix obtained from X with the mean translated to zero and the variances normalized to 1.

3.3.1 Inference Based on the KL TRANSFORM

The discrete KL-transform, also called the Hotelling transform, is determined as follows. S=UΣV^(T) is the singular value decomposition of S, where U and V are orthonormal and Σ is diagonal and nonnegative. The diagonal elements, σ_(j)=Σ(i,j), are referred to as the singular values of S and they are nonincreasingly ordered. The transformed variables in the transformed data Y=SV are uncorrelated and ordered with nonincreasing variances σ² _(j). This is verified by checking that Y^(T)Y=Σ³ is diagonal. It is also possible to verify that the transformed variables remain zero means. The KL-transform is applied often for the so-called Principal Component Analysis (PCA) where the number of decorrelated variables with dominant variances (i.e., the principal components Y_(p)) is significantly smaller than the number of the original variables.

The samples of a source distribution ƒ, with the mean shifted to zero, is represented more compressly in the principal components, Y_(p)f_(p). H is designed to determine the principal components. The principal coefficients f_(p) are then determined by the following equation g=(HY _(p))f _(p).

The system of equations in (12) is no longer underdetermined and is well conditioned, (i.e., f_(p) can be uniquely determined and stably computed). To get the mean value of the samples, ƒ_(m), H is designed so that the total sum of the samples can be measured, which is feasible in many practical situations. The samples are then estimated by Y_(p)f_(p)+ƒm.

4. Nonlinear Signal Inference

Nonlinear signal inference consists of estimating f_(⊥) by combining the measured value of f_(∥) with prior knowledge of the signal subspace V_(f). Formally, the estimated signal is f _(est) =f _(∥est) +f _(⊥est)(f _(∥est))  (13) As discussed earlier, nonlinear inference is attractive when there exists a one-to-one mapping between f_(c) and f_(∥) even though V_(f) is not a linear projection of V_(c). In this case, f_(∥) is completely indicative of f_(c) and f_(⊥est) (f_(∥est)) is the point in V_(⊥) indicated as a component of f_(c).

The primary challenges of nonlinear inference are characterization of V_(f), discovery of a linear mapping H such that V_(∥) is indicative of V_(f), and development of an algorithm associating each point in V_(∥) with a corresponding point in V_(f). While solutions to these challenges have been demonstrated in highly constrained situations, such as wavemeters and tracking systems, exact solutions are unavailable in general spectroscopy and imaging systems.

Specification of f_(⊥est) (f_(∥est)) is challenging because for broad natural signal classes, such as optical spectral and images, it is not possible to precisely describe V_(ƒ). One embodiment of the present invention is a method of overcoming this challenge using Bayesian inference. Bayesian inference is based on statistical characterization of V_(f). Distributions for components of f given measurement states are estimated from a library of known signals. These distributions are then used to determine likely signal states given measurements from unknown signals. Bayesian inference produces an estimate corresponding to the most likely value of f_(c) given the measurement g. System design for Bayesian inference consists of choosing H such that the most likely signal corresponds to the actual signal for a given measurement. In practice, global maximization of the probability given a measurement state is less practical.

Another embodiment of the present invention is a method using objective inference. Objective inference is based on plausibility measures of f_(est). Plausibility is expressed in one or more “objective functions,” θ(f_(est)). Objective functions are measures of signal continuity, smoothness, or coherence. Objective inference selects f_(⊥est) (f_(∥est)) to maximize objective measures of f_(est). The challenge of objective inference is to discover a mapping H and an objective function such that f_(est) is a good estimate of f_(c).

Another embodiment of the present invention is a method using graphical inference. Graphical inference constructs objective functions from empirical samples drawn from V_(f). By constructing empirical distributions and objective measures graphical inference is viewed as an integration of Bayesian and objective methods.

5. Compressive Spectroscopy

Physical implementations of compressive sampling include but are not limited to spectroscopy, digital imaging and temporal signal analysis. A spectrometer measures a set of “spectral projections” of the form m[n]=S(λ),h _(n)(λ)

.  (14) Multiplex spectroscopy under which h_(n) corresponds to a Fourier component or a Hadamard/Walsh code is common. More generally, h_(n) represents a filter function. Almost any real positive valued filter function can be implemented. Bipolar and complex filters are constructed from linear transformations of real positive filters.

FIG. 8 is a schematic diagram of an exemplary spectrometer 800 employing compressive sampling, in accordance with an embodiment of the present invention. Spectrometer 800 includes spectral source 810, diffraction grating 820, lens 830, mirror 840, mask 850, complementary mirror 860, complementary lens 870, complementary diffraction grating 880, and detector array 890. Unknown spectral source 810 is imaged onto a slit at the input. Spectral source 810 is assumed to uniformly illuminate the slit. If spectral source 810 is a point source, a combination of cylindrical lenses can be used to expand spectral source 810 to fully illuminate the slit normal to the plane of FIG. 8 while keeping spectral source 810 focused in the plane of the FIG. 8. The input slit is imaged onto mask 850 through diffraction grating 820 or a hologram. The effect of diffraction grating 820 is to separate the image by color component. In spectrometer 800, blue is imaged at the top of the mask, green in the middle, and red at the bottom.

Mask 850 is divided into rows. Each element in a row is a filter component, modulating the color channel corresponding to the corresponding column. The modulation is by absorption, meaning that the filter weights are all positive. Negative or complex filter weights are obtained by linear weighting of the output filtered signals. When the slit is re-imaged through complementary grating 880 onto output detector array 890, each element on the detector array consists of a filtered projection of the input spectrum. Since the filter function corresponds exactly to the transmittance of the mask, any filtering operation is implemented by spectrometer 800. Thus, it is possible to implement a compressive sampling scheme in spectrometer 800.

5.1 Haar-Wavelet Based Spectral Sampling

Another embodiment of the present invention is a method for compressive sampling using Haar-wavelet based spectral sampling. The target spectra consists of discrete projections $\begin{matrix} {{s\lbrack n\rbrack} = \left\langle {{S(\lambda)},{{rect}\left( \frac{\lambda - {n\quad{\delta\lambda}}}{\delta\lambda} \right)}} \right\rangle} & (15) \end{matrix}$ The resolution of spectrometer 800 is δλ and there are N spectral channels. So the slits in mask 850 are designed to pass spectral signals of width δλ on to detector array 890. The slits are of discrete width so that measurements on spectrometer 800 take the form $\begin{matrix} {{\overset{\_}{m}\left\lbrack n^{\prime} \right\rbrack} = {\sum\limits_{n}{\alpha_{n^{\prime}n}{s\lbrack n\rbrack}}}} & (16) \end{matrix}$

Using a binary mask, σ_(n′n) assumes values 0 or 1. However, within each row (corresponding to the n′^(th) measurement) any sequence σ_(n′n) can be implemented. In another embodiment of the present invention and in order to maximize throughput, Hadamard S-Matrix codes of s[n′l] are implemented in each mask row. For example, $\begin{matrix} {{s\left\lbrack {n^{\prime},l} \right\rbrack} = {\sum\limits_{n = {{{({n^{\prime} - 1})}l} + 1}}^{n = {n^{\prime}l}}{s\lbrack n\rbrack}}} & (17) \end{matrix}$

Compressively sampled spectroscopy consists of making measurements of the form $\begin{matrix} {{m\left\lbrack n^{\prime} \right\rbrack} = {{\sum\limits_{n = 1}^{l{({J,N})}}{{a_{J}\left\lbrack {n^{\prime},n} \right\rbrack}f\quad{a_{j}\lbrack n\rbrack}}} + {\sum\limits_{j = 1}^{J}{\sum\limits_{n = 1}^{l{({j,N})}}{{d_{j}\left\lbrack {n^{\prime},n} \right\rbrack}f\quad{d_{j}\lbrack n\rbrack}}}}}} & (18) \end{matrix}$ where ƒα_(J)[n] describes the signal averages at level J using a Haar-wavelet transform, ƒd_(J)[n], 1≦j≦J describes the Haar details for levels 1 through J. In another embodiment of the present invention, the following measurements are made. At level J, 2^(−J)N averages are measured (i.e., α_(J)[n′,n]=δ_(nn′)). At level J, 2^(−J)N details are also measured (i.e., d_(J)[n′,n]=δ_(nn′)). For levels 1≦j≦J, 2^(−J)N values of sums of some permutations of details are measured, (i.e., ${d_{j}\left\lbrack {n^{\prime},n} \right\rbrack} = {\sum\limits_{n_{p} = 1}^{2{({J - j})}}\delta_{{nf}{({n,n_{p}})}}}$ where ƒ(n,n_(p)) is a perturbation of signal positions n′>N).

FIG. 9 is a plot of an exemplary spectral transmission mask 900 used to make measurements on Haar-wavelet averages and details, in accordance with an embodiment of the present invention. Mask 900 measures 256 spectral channels with just 80 measurements, implementing 31.25% compression.

Mask 900 makes measurements using a “lifting” procedure. A Haar-wavelet transform is implemented with the following direct transform: $\begin{matrix} {{A_{J + 1}\lbrack n\rbrack} = \frac{{A_{J}\left\lbrack {{2n} - 1} \right\rbrack} + {A_{J}\lbrack n\rbrack}}{\sqrt{2}}} & (19) \\ {{D_{J + 1}\lbrack n\rbrack} = \frac{{A_{J}\left\lbrack {{2n} - 1} \right\rbrack} - {A_{J}\lbrack n\rbrack}}{\sqrt{2}}} & (20) \end{matrix}$ where A_(j) and D_(j) are the average and detail coefficients at level j. The inverse transform is given by $\begin{matrix} {{A_{J}\left\lbrack {{2n} - 1} \right\rbrack} = \frac{{A_{J + 1}\lbrack n\rbrack} + {D_{J + 1}\lbrack n\rbrack}}{\sqrt{2}}} & (21) \\ {{A_{J}\left\lbrack {2n} \right\rbrack} = \frac{{A_{J + 1}\lbrack n\rbrack} - {D_{J + 1}\lbrack n\rbrack}}{\sqrt{2}}} & (22) \end{matrix}$

Mask 900 measures all 32 average coefficients at level 3. The Haar average coefficients are the sum of spectral channels of appropriate length and scaled to an appropriate factor depending on the level. These averages at level 3 are used to find the averages and details at level 4 using the direct transform defined in Eq. (19).

For the remaining measurements, measurements of sums of combinations of details for levels 1 through 3 are made. Specifically, sums of 2 level 3 details, sums of 4 level 2 details and sums of 8 level 1 details are measured. Since averages can be easily measured with mask 900, sums of the averages at each level are measure and the sums of details are then estimated with the prior knowledge of all the parent details and parent averages.

Consider the measurement m_(j)[n] produced by sensor n at level j given by $\begin{matrix} {{m_{j}\lbrack n\rbrack} = {\alpha_{j}{\sum\limits_{k}{A_{j}\lbrack k\rbrack}}}} & (23) \end{matrix}$ where k includes all the averages selected by the mask for the measurement at n. From the definition of the Haar transform, the proportional factor α_(j) at level j is given by α_(j)=√{square root over (2^(j))}  (24) The averages at level j are related to the averages and details at Level j+1 as given in Eq. 21. Therefore, $\begin{matrix} {\frac{m_{j}\lbrack n\rbrack}{\alpha_{j}} = {{\sum\limits_{k^{\prime}}{A_{j + 1}\left\lbrack k^{\prime} \right\rbrack}} + {\sum\limits_{k^{\prime}}{D_{\quad{j\quad + \quad 1}}\left\lbrack k^{\quad\prime} \right\rbrack}}}} & (25) \end{matrix}$ where k′ represents the parent averages and details corresponding to index k. $\begin{matrix} {{\sum\limits_{k^{\prime}}{D_{j + 1}\left\lbrack k^{\prime} \right\rbrack}} = {\frac{m_{j}\lbrack n\rbrack}{\alpha_{j}} - {\sum\limits_{k^{\prime}}{A_{j + 1}\left\lbrack k^{\prime} \right\rbrack}}}} & (26) \end{matrix}$

The sum of the details in Eq. 26 is then assigned to each of the individual detail coefficient based on a weighting scheme. Specifically, a proportional weighting scheme is used in all the reconstructions.

FIG. 10 is a flowchart showing an exemplary method 1000 for reconstructing Haar-wavelet averages and details for mask 900 shown in FIG. 9, in accordance with an embodiment of the present invention. Method 1000 can be used for any mask.

FIG. 11 is a plot of an exemplary comparison 1100 of original neon spectral data and reconstructed neon spectral data using the exemplary method 1000 shown in FIG. 10, in accordance with an embodiment of the present invention.

FIG. 12 is a plot of an exemplary comparison 1200 of original neon wavelet coefficient data and reconstructed neon coefficient data using the exemplary method 1000 shown in FIG. 10, in accordance with an embodiment of the present invention.

5.1 Measurement Efficient Optical Wavemeters

Another embodiment of the present invention is a multiplex optical sensor that efficiently determines the wavelength of a quasi-monochromatic source. The measurement efficient optical wavemeter is a special case of a general spectrometer based on compressive sampling in that the source is assumed to have only one spectral channel.

5.2.1 Theory

A digital optical spectrometer estimates features of the spectral density, S(λ) of a source from discrete measurements. Measurements consist of spectral projections of the form m[n]=∫h _(n)(λ)S(λ)dλ,  (27) where n is the index of the discrete array of measurements. Measurements are taken at different points in time (e.g. serially) or at different points in space (in parallel), or in some spatial and temporal combination. Measurements taken at different points in time rely on one or just a few optical detectors, with changes in the optical system accounting for variation in h_(n)(λ) from one measurement to the next. Measurements taken in parallel require an array of optical detectors.

An optical wavemeter characterizes S(λ) under a prior constraint that only one spectral channel is active. This assumption is expressed as S(λ)=αδ(λ−λ_(o)),  (28) where α is the amplitude of the signal at λ=λ_(o). In this case, the measurements are: m[n]=αh _(n)(λ_(o))  (29)

The goal of the wavemeter is to estimate α and λ_(o) from the measurements m[n]. The number of possible outcomes from the estimation process is D_(α)N_(λ), where D_(α) is the dynamic range estimated in α, and N_(λ) is the number of possible values of λ_(o). The number of possible outcomes from the measurement process is D_(m) ^(N) ^(m) , where D_(m) is the measurement of dynamic range, and N_(m) is the number of measurements. If the sensor system is designed to achieve a one-to-one mapping between measurement outcomes and sensor outcomes, the number of measurements required is $\begin{matrix} {N_{m} \geq \frac{{\log\quad D_{\alpha}} + {\log\quad N_{\lambda}}}{\log\quad D_{m}}} & (30) \end{matrix}$

As an example of a maximally efficient sensor, suppose that D_(α)=D_(m). For a monochromatic source, α is measured using a single measurement with h₁(λ)=1. log N_(λ)/log D_(m) additional measurements are needed to determine λ_(o). If N_(λ)≦log D_(m) then only one additional measurement is needed. If h₂(λ)=κ(λ−λ_(min)) is selected for the second measurement, where κ is a constant, then: m[2]=ακ(λ−λ_(min))  (31)

α is already known from m[1] and the dynamic range is assumed sufficient to discriminate N_(λ) spectral bins in the range from m[2]=0 to m[2]=ακ(λ_(max)−λ_(min)).

While the method described by Eq. (31) shows that it is possible to build an optical wavemeter with just two measurements, it also illustrates that high dynamic range and a carefully designed and characterized sensor spectral response are necessary to achieve such efficiency. In practice, it is difficult to implement a filter-detector combination with perfectly linear spectral response. Efficient wavemeter design requires specification of both the dynamic range of the measurement system and control over the spectral responses of the measurements.

Suppose that h_(n)(λ) is independently specified within each wavelength channel to a dynamic range of D_(h). Possible values for h_(n)(λ) range from 0 to h_(n max). Assuming that the dynamic range of the measurement system is sufficient for all values of h_(n), Eq. (29) produces D_(h) ^(N) ^(m) different measurement states. Assuming that α has been characterized by a single measurement as before, N_(m)−1 additional measurements characterize λ_(o) if D_(h) ^(N) ^(m−1) ≧N_(λ), or N _(m)≧1+log_(D) _(h) N _(λ)  (32)

Another embodiment of the present invention is a method for achieving the limit specified in Eq. (32). The method assumes that the N_(λ) possible outcomes for estimation of λ_(o) are ordered and assigned indices n(λ) such that 1≦n(λ)≦N_(λ). This ordering is such that n(λ) is monotonic in λ−λ_(min), but such proportionality is not necessary and physical design constraints may make some other ordering attractive. Let d(n, l, b) be the l^(th) digit of n in the base b. A filter array such that $\begin{matrix} {{{h_{n}(\lambda)} = {d\left( {{n(\lambda)},n,D_{h}} \right)}}\frac{h_{n\quad\max}}{D_{h}}} & (33) \end{matrix}$ enables characterization of λ_(o) in N_(λ)/log D_(h) measurements. The method has been described in Eq. (31) for the case D_(h)=N_(λ), yielding N_(m)=2. In the more general case, comparing of Eqs. (29) and (33) it can be seen that m[n]/(αh_(n max)) is the n^(th) digit in the base D_(h) representation of n(λ_(o)).

Under the method described earlier, h_(n)(λ) is programmed using transmission masks in a grating-based Fourier filter system. The transmittance of such masks is normally determined in a multiple step coating process. The number of transmittance values is typically D_(h)=2^(steps), where steps is the number of deposition steps. With 5 deposition steps, for example, Eq. (32) suggests that a 1024 channel source can be characterized using just 3 measurements. With just one deposition step, a 1024 channel source is characterized in 11 measurements.

Exemplary demonstrations of optical wavemeters for D_(h)=2 and D_(h)=4 are provided.

5.2.2 Design

As described above, a key step in the compressive wavemeter is the imprinting of a spatial-spectral intensity code on the source. A subsequent measurement of the spatial variation of the intensity, coupled with knowledge of the spatial-spectral code, then allows reliable estimation of the input spectrum.

FIG. 13 is a schematic diagram showing an exemplary optical wavemeter 1300, in accordance with an embodiment of the present invention. Wavemeter 1002 includes source 1305, slit 1310, lens 1315, diffraction grating 1320, lens 1325, intensity encoding mask 1330, lens 1335, diffraction grating 1340, lens 1345, and linear detector array 1350. Wavemeter 1300 utilizes a one-dimensional spatial-spectral intensity code. In other words, 1-D positional variation in intensity is produced that is a function of wavelength.

This encoding is not produced directly, but instead as the result of a three stage process. First, diffraction grating 1320 converts the spectral density of source 1305 into a spatially-varying intensity profile. Because source 1305 is monochromatic and grating 1320 is located in the center of a 4-f imaging system, this produces an image of the input slit in the image plane at a horizontal position that is wavelength dependent.

Second, mask 1330 with a 2-D pattern of transmission variations is placed in the image plane. The image of slit 1310 interacts with a narrow horizontal region of mask 1330 and is intensity-modulated by the vertical transmission pattern of mask 1330 at that horizontal location. Because the horizontal image location is wavelength dependent, a horizontal variation in the mask transmission produces different vertical intensity patterns for different spectral channels.

Finally, diffraction grating 1340 and imaging setup undo the correlation between spectral channel and horizontal position, producing a stationary image of slit 1310 regardless of wavelength. The vertical variation of intensity that was imprinted by mask 1330, however, remains. By arranging linear detector array 1350 along the vertical direction of the slit image, this intensity variation can be measured. Using the knowledge of the transmission pattern on mask 1330 and the dispersion properties of grating 1320 and grating 1340, the spectral channel of source 1305 is uniquely determined.

In another embodiment of the present invention, linear detector array 1350 is a two-dimensional array or a single element detector. In another embodiment of the present invention, the linear detector array 1350 is a base-N encoded mask and measures N levels of intensity. In another embodiment of the present invention, mask 1330 is a single element photodiode and is dynamically variable.

Another embodiment of the present invention is a method of creating transmission mask 1330. As described earlier, if it is assumed that detectors 1350 have m distinguishable output levels, then the optimal code for N spectral channels requires 1+log_(m) N measurements. These measurements determine not only the spectral channel of the source, but also its power. (For the special case of m=2, the spectral channel alone can be determined by log₂ N measurements, but a similar reduction is not possible for other values of m.)

If N spectral channels are numbered from 1 to N, the problem then becomes isomorphic to an m-ary search tree. The k-th measurement is used to determine the k-th m-ary digit in the representation of the channel number. Thus, mask 1330 has a vertical layer for each measurement (and hence, sensor) required by the encoding. Each layer subdivides each region of the layer above into m sub-regions, each with one of m levels of transmission.

FIG. 14 is a plot of an exemplary mask 1400 for a 256-channel wavemeter with binary sensors, in accordance with an embodiment of the present invention. White regions are completely transparent, while black regions are completely opaque. Note that this is an example of the special case for m=2, where the spectral channel can be determined but not the power.

To work with non-binary sensors requires multiple levels of transparency. Producing masks with “grayscale” transparency is more difficult than producing simple binary masks. However, the finite vertical height of individual mask rows can be used to modulate the transmission to intermediate values, while still using only fully transmissive or fully opaque coatings.

FIG. 15 is a plot of an exemplary mask 1500 for a 256-channel wavemeter with 4-level sensors, in accordance with an embodiment of the present invention. Again, white regions are completely transmissive and black regions are completely opaque. The top line of mask 1400 provides information about the signal power, and also provides a reference level for the remaining rows.

5.2.3 Experimental Results

Another embodiment of the present invention is an exemplary method of building wavemeter 1300 shown in FIG. 13 for use with mask 1400 of FIG. 14 and mask 1500 of FIG. 15. Wavemeter 1300 has a resolution substantially equivalent to one nanometer. Mask 1400 and mask 1500 are designed to resolve 256 wavelengths with either four or eight measurements.

The resolution and the wavelength range of wavemeter 1300 is determined by the smallest feature size that can be implemented in either mask and also by the focal length ‘f’ of the lenses and the grating period ‘Λ’. For a grating with period Λ, the primary grating equation for diffraction order m is $\begin{matrix} {{{\sin\quad\theta_{i}} + {\sin\quad\theta_{d}}} = \frac{m\quad\lambda}{\Lambda}} & (34) \end{matrix}$ where θ_(i) and θ_(d) are the incidence and diffraction angles respectively. For a given center wavelength of operation λ_(c), this equation determines the operating diffraction angle at a particular incidence angle. The grating used has 600 lines per mm and the wavemeter is designed for a center wavelength of 1520 nm. With these values and for normal incidences, the diffraction angle is 65.8°.

The angular dispersion of the grating is found by differentiating Eq. 34 $\begin{matrix} {{\Delta\theta}_{d} = \frac{m\quad{\Delta\lambda}}{\Lambda\quad\cos\quad\theta_{d}}} & (35) \end{matrix}$ and the linear dispersion of the grating on the mask is given by Δx=ƒΔθ_(d)  (36) where f is the focal length of the lens in the 4-f imaging system. These dispersion equations determine the feature size of the masks used. A 4-f imaging system uses 50 mm focal length lenses.

An Agilent Tunable laser source 81618A, for example, is used as the input test source 1305. Laser source 1305 has an operating wavelength range of 1466-1586 nm. The range is further restricted to 1470-1570 nm (i.e., 100 channels with a 1 nm even though the masks can resolve up to 256 channels). An Indigo Systems AlphaNIR camera, for example, is used as detector array 1350. Here, the NIR camera is used as only an 8-element linear detector measuring only the intensity on each detector.

5.2.4 Binary Mask Results

Another embodiment of the present invention is wavemeter 1300 of FIG. 13 including binary mask 1400 of FIG. 14. An Agilent tunable laser source, for example, is used as the test source 1305. The output from the laser source is tuned to each of the 100 wavelengths in the 1471-1570 nm range. The fiber coupled output from the source 1305 is collimated and a vertical slit of size 25 μm is placed after the collimator. Slit 1310 is imaged on to the mask patterns of mask 1300 using a 4-f imaging system and through a diffraction grating, for example. Due to grating 1320, the slit that is imaged onto mask 1400 falls at different locations and hence gets spectrally coded differently based on the input wavelength. These patterns uniquely determine the wavelength of input source 1305.

FIG. 16 is a depiction of an exemplary sample image 1600 taken using exemplary optical wavemeter 1300 of FIG. 13 and the exemplary mask 1400 of FIG. 14, in accordance with an embodiment of the present invention. For diagnostic purposes, the output plane of wavemeter 1300 is imaged rather than letting it strike linear detector array 1350. The clear representation of the bit-pattern “10001100” is seen image 1600. The analysis of the data is done by simulating the presence of a linear array in the detection plane. That is, for each simulated photodetector, a pixel region is designated in the image. Further, these regions are of identical size and shape, and are regularly spaced along a line.

FIG. 17 is a plot of exemplary expected bit patterns 1700 for each of 100 wavelengths using exemplary optical wavemeter 1300 of FIG. 13 and exemplary mask 1400 of FIG. 14, in accordance with an embodiment of the present invention. Again, the 100 nm (1471-1570 nm) scan range is due to the limited tunability of source 1305.

FIG. 18 is a plot of exemplary measured bit patterns 1800 for each of 100 wavelengths using the exemplary optical wavemeter 1300 of FIG. 13 and the exemplary mask 1400 of FIG. 14, in accordance with an embodiment of the present invention. The 100 wavelength channels are scanned and the bit patterns obtained on each of the 8 detector element regions on the NIR camera are measured. 82 unique patterns are observed from the measurements.

FIG. 19 is a plot of exemplary measured errors 1900 in bit patterns for each of 100 wavelengths using exemplary optical wavemeter 1300 of FIG. 13 and exemplary mask 1400 of FIG. 14, in accordance with an embodiment of the present invention. Measured errors 1900 are the absolute values of the error patterns. Errors are chiefly observed in the 7th and 8th bits because of the small feature sizes involved in mask 1400. Also, some errors are caused by the well-known curvature of a slit imaged through a grating. This slight curvature is seen in image 1600 of FIG. 16 too. As a result, the light (especially in the high resolution areas of the mask) is masked as if it is in a neighboring spectral channel. These errors can be corrected in modifying mask 1400 so that it has a slight arc to match the expected curvature of the slit image.

5.2.4 Base 4 Mask Results

Another embodiment of the present invention is wavemeter 1300 of FIG. 13 including 4-level mask 1500 of FIG. 15. Mask 1500 is also designed for 256 wavelength channels. Again a laser source 1305 restricts testing only 100 wavelength channels (the 1471-1570 nm range).

FIG. 20 is a plot of exemplary quaternary digit patterns 2000 for each of 100 wavelengths using exemplary optical wavemeter 1300 of FIG. 13 and exemplary mask 1500 of FIG. 15, in accordance with an embodiment of the present invention. Mask 1500 is a base-4 encoded mask. The values are indicated by the colorbar. The calibration row is suppressed in FIG. 20.

FIG. 21 is a plot of exemplary measured quaternary digit patterns 2100 for each of 100 wavelengths using exemplary optical wavemeter 1300 of FIG. 13 and exemplary mask 1500 of FIG. 15, in accordance with an embodiment of the present invention. The measured intensity at each sensor is assigned to either 3, 2, 1 or 0 based on a threshold relative to the total power as determined by an additional 5th sensor. The values are indicated by the colorbar. The calibration row is suppressed in FIG. 20.

FIG. 22 is a plot of exemplary measured errors 2200 in quaternary digit patterns for each of 100 wavelengths using exemplary optical wavemeter 1300 of FIG. 13 and exemplary mask 1500 of FIG. 15, in accordance with an embodiment of the present invention. Measured errors 2200 are the absolute values of the error patterns. Again measured errors 2200 are due to the curvature, which could be corrected by compensating for it in the encoding mask.

5.3 Compressive Sampling of Temporal Signals

Another embodiment of the present invention is a method for compressive sampling of a temporal signal. A temporal signal includes but is not limited to a communications signal, radio wave, or video image pixel. For a temporal signal, measurements are taken as m[n]=s(t),h _(n)(t)

.  (37)

In this case, h_(n)(t) is, for example, implemented electrically. Since an electrical circuit can be designed to multiply the input signal by any function h_(n)(t), arbitrary compressive sampling is implemented for temporal signals using an electrical circuit.

FIG. 23 is a flowchart showing an exemplary method 2300 for temporally compressively sampling a signal, in accordance with an embodiment of the present invention.

In step 2310 of method 2300, a plurality of analog to digital converters is assembled to sample the signal. Each analog to digital converter of the plurality of analog to digital converters is configured to sample the signal at a time step determined by a temporal sampling function.

In step 2320, the signal is sampled over a period of time using the plurality of analog to digital converters. Each analog to digital converter of the plurality of analog to digital converters produces a measurement resulting in a number of measurements for the period of time.

In step 2330, the number of estimated signal values is calculated from the number of measurements and the temporal sampling function. The temporal sampling function is selected so that the number of measurements is less than the number of estimated signal values.

6. Compressive Imaging

Another embodiment of the present invention is a method for compressive imaging. For an image, compressive sampling consists of measuring projections of the form m[n _(x) ,n _(y)]=

(ƒ(x,y),h _(n) _(x) _(,n) _(y) (x,y)

.  (38)

The challenge in an imaging system is to obtain non-local response in h_(n) _(x) _(,n) _(y) (x,y). Method for programming h_(n) _(x) _(,n) _(y) (x,y) include but are not limited to setting the shape and interconnectivity of electronic pixels on a focal plane and/or by masking pixels, replicating the input image multiple times ƒ(x,y) over redundant apertures and by sampling these resulting images with different focal plane and/or mask structures, and using optical fan-out through birefringent structures, micro-optical-mechanical devices or diffraction gratings. Through a combination of these three mechanisms and linear operations on the output samples, it is possible to implement arbitrary image sampling functions.

6.1 Background

Another embodiment of the present invention is a parallel bank of imaging systems that is used to synthesize a single global image. The primary motivating factor for this embodiment is a reduction in the form factor of the imaging system. In particular, a system consisting of N sub-apertures rather than a single coherent aperture is approximately N times thinner than a single lens system. Using compressive sampling, it is possible to also reduce the transverse area of such systems in comparison to non-encoded imagers.

More generally, focal plane coding and compressive sampling enable superior data acquisition and handling in imaging systems. Physical layer data compression reduces the data transfer requirements off of the focal plane without reducing image resolution. This may be applied in achieving imaging systems with diffraction limited image resolution without electronic sampling at the diffraction limit. Focal plane coding and compressive sampling imaging also enables superior color management, including multispectral and hyperspectral imaging at reasonable data rates. Focal plane coding and compressive sampling can also be used in developing polarization cameras. Focal plane coding and compressive sampling imaging strategies can also be applied in temporal focal plane sampling to improve data rates and light sensitivity in video imaging systems.

The focal plane is the interface between the optical field and digitized image data. The theory of analog-digital conversion across such interfaces began half a century ago with the Whitakker-Shannon sampling theorem. From the perspective of optical imaging, Whittaker-Shannon indicates that image resolution is proportional to the focal plane sampling rate. A quarter century ago, generalized sampling theory showed that it was possible to reconstruct band-limited signals by sub-Nyquist sampling of parallel multi-channel linear transformations. Many investigators have applied sampling theory to digital image super-resolution.

Focal plane coding and compressive sampling imaging is based on two improvements to digital super-resolution techniques. The first improvement is sampling function optimization. While the importance of the sampling functions to reconstruction fidelity has been clear from the origins of generalized sampling, only translation, rotation, and scale have been realized for image super-resolution.

Another embodiment of the present invention is a method of using multiplex focal plane encoding to optimize linear transformations for image reconstruction. Focal plane coding is chosen over aperture or wavefront coding, because previous experience with multiplex imaging systems has shown that for conventional imaging tasks the highest entropy measurement states are obtained from compact impulse response transformations of the focal plane. An emphasis on adaptation of imaging system design to the statistics of specific imaging tasks is outlined in the second improvement.

The second improvement is compressive sampling. Fourier-space sampling theorems assume that information is uniformly distributed across the image signal space. The success of image compression algorithms over the past decade has emphatically demonstrated that this assumption is incorrect. Recognition that image data is spatially and, in the case of video, temporally correlated on multiple scales enables dramatic compression, particularly with the development of multiscale fractal and wavelet compression bases. Using delocalized multi-scale pixel responses, focal plane coding enables direct integration of compressive coding in the image acquisition layer, thereby enabling dramatic improvements in the data and computational efficiency of digital super-resolution. Another embodiment of the present invention is an optical sensor employing compressive sampling that tracks a source over N pixels using only log(N) electronic samples.

6.2 Imaging Model

In a model conventional digital imaging system, the transformation between the optical image ƒ(x,y) and the focal plane data g_(ij) is g _(ab) =∫∫∫∫p(x−aΔ,y−bΔ)h(x−x′,y−y′)ƒ(x′,y′)dxdydx′dy′  (39) where p(x,y) is the pixel sampling distribution on the focal plane, A is the pixel spacing and h(x,y) is the imaging system point spread function.

Another embodiment of the present invention is a method for measuring multiple demagnified copies of ƒ(x,y) and integrating computationally these copies into the original full resolution image. Multiple image copies are gathered by measuring the same object over multiple imaging systems, typically using a lenslet array. The final integrated signal is referred to as “the image” and the copies gathered during the measurement process are referred to as “subimages.” A conventional imaging system has an aperture for collecting the field. In this embodiment the aperture is segmented into multiple “subapertures” for each subimaging system.

Measurements under this embodiment take the form g _(ab) ^(k) =∫∫∫∫p _(abk)(x,y)h _(k)(x,x′,y,y′)ƒ(mx′,my′)dxdydx′dy′  (40)

where k refers to the subimage gathered over the k^(th) subaperture. p_(abk)(x,y) is the measurement sampling function for the ab^(th) pixel in the k^(th) subimage. h_(k)(x,x′,y,y′) is the point spread function mapping from the density at (x′,y′) in the image to the optical intensity at (x,y) on the focal plane in the k^(th) subimage. In contrast with the conventional imaging system, this embodiment allows for the possibility that the pixel sampling function and the point spread functions are shift variant. Shift variance is important in creating non-redundant codes between subimages.

The subimages must be non-redundant to enable well-conditioned full resolution reconstruction of the image ƒ(x,y). Non-redundant coding between sub-images is, for example, achieved by the following mechanisms.

One mechanism involves employing image shifts. Simple shifts in the optical image relative to the focal plane from one subaperture to the next make the system weakly non-redundant. This approach is used in TOMBO imaging and similar systems. The TOMBO system demonstrates nondegenerate coding of multiple subimages but does not demonstrate compressive sampling of signal inference. Variations in the registration of the pixel sampling function relative to the subimage from subaperture to subaperture is the core of this mechanism.

Another mechanism involves using birefringent shift filters. Birefringent shift filters allow controlled and self-registering image shifts and also allow multiplexed image acquisition and reconstruction. Modulation of the point spread function from subimage to subimage is at the core of this mechanism.

Another mechanism involves using sampling masks. Sampling masks are placed in the focal plane to vary p(x,y) from one sub-image to the next. Variations in the spatial structure of the pixel sampling function from subimage to subimage is at the core of this mechanism.

Another mechanism involves using microlenslet and microprism arrays. Microlenslet and microprism arrays are be used to remap the optical focal plane from subimage to subimage. This mechanism modulates the point spread function in a shift-variant manner.

Another mechanism involves using spectral and spatial filters. Spectral filters, such as gratings and Fabry-Perot and Fizeau Filters can remap the spatio-spectral pattern of the image from one sub-aperture to the next. This mechanism modulates the structure of the point spread function, typically in a chromatically varying fashion.

Another mechanism involves the physical structure of the focal plane and focal plane read dynamics. The physical structure of the focal plane and focal plane read dynamics may vary from one subimage and from one pixel to the next. This mechanism is similar to foveation. This mechanism modulates the structure of the pixel sampling function more profoundly than can be done using simple pixel masks.

Another mechanism involves using micro-optical mechanical devices. Micro-optical mechanical devices, such as deformable mirror arrays, digital light projectors, and similar dynamic elements are used to encode the optical impulse response for compressive sampling. Liquid crystal or electro-optic spatial light modulators might are similarly applied to effectively combine the functions of coding masks and micro-optical elements. Dynamic spatial light modulators are used to adapt sensor encodings to improve compressive sampling.

6.3 Coding Mechanisms

Another embodiment of the present invention is a focal plane coding and compressive sampling imaging system that measures linear transformations of an image ƒ(x,y) such that the full image is digitally reconstructed. This embodiment uses optical and electro-optical prefilters to encode the linear transformations to enable and improve the fidelity of image reconstruction.

While in some cases this embodiment is used in single aperture systems, in most cases this embodiment involves encoding over multiple subapertures. In this case, the imaging system is regarded as a filter bank and/or sampling system array.

6.3.1 Pixel Shift Coding

Another embodiment of the present invention is a method for pixel shift coding. This method can be used individually to encode images for reconstruction or can be used in combination with other methods. Pixel shift coding consists of measurement under different pixel shifts from one subaperture to the next. The measurement model for this case is g _(ab) ^(k) ^(x) ^(k) ^(y) =∫∫∫∫p(x−aΔ−k _(x) δ,y−bΔ−k _(y)δ)h(x−x′,y−y′)ƒ(Mx′,My′)dxdydx′dy′  (41) where δ is a subimage to subimage differential shift. While it is assumed here that there is a constant differential shift, the differential pixel shift, in practice, can vary nonlinearly and image rotations can also be considered from one subimage to the next.

Pixel shift coding has been previously considered by many. Some, in particular, have considered the use of pixel shift coding using multiple subimage arrays. Registration of the subimages relative to the global image coordinates has been a primary challenge for these methods. Others have focused on interpolation of full images based on self-registration using correlations between captured images.

The total pixel shift from subimage to subimage is due to shifts in the registration of the optical axis of each subaperture relative to the electronic pixel axis. If, for example, the subapertures are arranged on a Cartesian grid with uniform spacing Δ_(lens) between optical axis in both x and y, the subimage differential shift is δ=mod(Δ_(lens),Δ)  (42) where mod( ) is the modulus function.

The total pixel shift from subimage to subimage is also due to shifts in the registration of the optical axis of each subaperture relative to the axis of the object. The differential shift due to the subaperture-object shift is $\begin{matrix} {\delta = {\Delta_{lens}\frac{F}{R}}} & (43) \end{matrix}$ where F is the focal length of the imaging lenslet and R is the object range.

The object range dependent differential shift makes it difficult to implement uniform coding using pixel shifts and requires the self-registration techniques used by several authors.

The value of the differential pixel shifts critically determines the conditioning of the subimages for full image estimation. Uniformly spaced shifts spanning the range of the pixel sampling period are particularly attractive. The simplest approach to understanding image estimation for shifted pixel imagers relies on expansion of p(x,y) on a finite basis. In typical systems, p(x,y) is a uniform rectangular function with finite fill factor. Assuming 100% fill factor it can be assumed that $\begin{matrix} {{p\left( {x,y} \right)} = {\beta^{o}\left( \frac{x}{\Delta} \right)}} & (44) \end{matrix}$ where β⁰( ) is the order 0 B-spline. If Δ/δ is an integer, the family of functions $\begin{matrix} {{\phi_{n}(x)} = {\beta^{o}\left( \frac{x - {n\quad\delta}}{\delta} \right)}} & (45) \end{matrix}$ spans the range of the pixel sampling functions specifically $\begin{matrix} {{p\left( {{x - {a\quad\Delta} - {k_{x}\delta}},{y - {b\quad\Delta} - {k_{y}\delta}}} \right)} = {\sum\limits_{n = {{{({a - \frac{1}{2}})}\frac{\Delta}{\delta}} + k_{x} + \frac{1}{2}}}^{n = {{{({a + \frac{1}{2}})}\frac{\Delta}{\delta}} + k_{x} - \frac{1}{2}}}{\sum\limits_{n = {{{({b - \frac{1}{2}})}\frac{\Delta}{\delta}} + k_{y} + \frac{1}{2}}}^{n = {{{({b + \frac{1}{2\quad}})}\frac{\Delta}{\delta}} + k_{y} - \frac{1}{2}}}{{\phi_{n}(x)}{\phi_{m}(y)}}}}} & (46) \end{matrix}$ where it is assumed that Δ/δ is even. Defining a canonical representation for the discrete image f_(c) as f _(c)(n,m)=∫∫∫∫φ_(n)(x)φ_(m)(y)h(x−x′,y−y′)ƒ(Mx′,My′)dxdydx′dy′  (47) Eqn. (41) is expressed in discrete form as g=Tf_(c)  (48) where T consists of N² _(ap) submatricies, one for each subimage. Each row of T contains)Δ/δ)² ones and all other components are zero. Although T is not particularly well conditioned, f_(c) is estimated by direct algebraic inversion or other methods.

A critical challenge for pixel shift methods, is that Δ/δ is not generally an integer, in which case there is no convenient finite basis for representation of f_(c). In this case, interpolation methods are required.

6.3.2 Birefringent Shift Coding

Another embodiment of the present invention is a method for birefringent shift coding. Birefringent coding elements having previously been used to produce coded optical fan-out. Each layer of a birefringent filter produces two orthogonally polarized copies of the incident field. It is assumed that the image field is unpolarized, although coding to estimate polarization image is also possible. The transformation with shift coding induced by a birefringent filter is $\begin{matrix} \begin{matrix} {g_{ab}^{k} = {\int{\int{\int{\int{{p\left( {{x - {a\quad\Delta}},{y - {b\quad\Delta}}} \right)}{h\left( {{x - x^{\prime}},{y - y^{\prime}}} \right)}}}}}}} \\ {\sum\limits_{n}{{f\left( {{{M\quad x^{\prime}} - {\delta\quad x_{nk}}},{{My}^{\prime} - {\delta\quad y_{nk}}}} \right)}{\mathbb{d}x}{\mathbb{d}y}{\mathbb{d}x^{\prime}}{\mathbb{d}y^{\prime}}}} \end{matrix} & (49) \end{matrix}$

This transformation is equivalently expressed in terms of the Fourier transforms of the pixel sampling function P(u,v), the point spread function (PSF) H(u,v), and the intensity distribution F(u,v) as $\begin{matrix} {g_{ab}^{k} = {\frac{1}{M^{2}}{\int{\int{{P\left( {u,v} \right)}{H\left( {u,v} \right)}{F\left( {\frac{u}{M},\frac{v}{M}} \right)}{\mathbb{e}}^{{- {\mathbb{i}}}\quad 2\quad\pi\quad u\quad a\quad\Delta}{{\mathbb{e}}^{{- {\mathbb{i}}}\quad 2\quad\pi\quad v\quad b\quad\Delta}\left\lbrack {\sum\limits_{n}{{\mathbb{e}}^{{- {\mathbb{i}}}\quad 2\pi\quad u\quad\delta\quad x_{nk}}{\mathbb{e}}^{{- {\mathbb{i}}}\quad 2\quad\pi\quad v\quad\delta\quad y_{nk}}}} \right\rbrack}{\mathbb{d}u}{\mathbb{d}v}}}}}} & (50) \end{matrix}$

The discrete Fourier transform (DFT) of the measured data is: $\begin{matrix} {G_{\alpha\quad\beta}^{k} = {{\frac{1}{N_{s}}{\sum\limits_{{ab} = 0}^{N_{s} - 1}{g_{ab}{\mathbb{e}}^{{- {\mathbb{i}}}\quad 2\pi^{\frac{a\quad\alpha}{N_{s}}}}{\mathbb{e}}^{{- {\mathbb{i}}}\quad 2\quad\pi^{\frac{b\quad\beta}{N_{s}}}}}}}\quad = {{\frac{1}{N_{s}M^{2}}{\int{\int{{\left\lbrack {{P\left( {u,v} \right)}{H\left( {u,v} \right)}{F\left( {\frac{u}{M},\frac{v}{M}} \right)}} \right\rbrack \times \quad\left\lbrack {{\mathbb{e}}^{{- {{\mathbb{i}\pi}{({N_{s} - 1})}}}{({\frac{\alpha}{N_{s}} + {u\quad\Delta}})}}{\mathbb{e}}^{{- {{\mathbb{i}\pi}{({N_{s} - 1})}}}{({\frac{\beta}{N_{s}} + {v\quad\Delta}})}}} \right\rbrack \times \quad\left\lbrack {\frac{\sin\left( {\pi\left( {\alpha + {N_{s}u\quad\Delta}} \right)} \right)}{\sin\left( {\pi\left( {\frac{\alpha}{N_{s}} + {u\quad\Delta}} \right)} \right)}\frac{\sin\left( {\pi\left( {\beta + {N_{s}v\quad\Delta}} \right)} \right)}{\sin\left( {\pi\left( {\frac{\beta}{N_{s}} + {v\quad\Delta}} \right)} \right)}} \right\rbrack \times \quad\left\lbrack {\sum\limits_{n^{\prime}}{{\mathbb{e}}^{{- {\mathbb{i}}}\quad 2\pi\quad u\quad\delta\quad x_{n^{\prime}k}}{\mathbb{e}}^{{- {\mathbb{i}}}\quad 2\pi\quad v\quad\delta\quad y_{n^{\prime}k}}}} \right\rbrack}{\mathbb{d}u}\quad{\mathbb{d}v}}}}}\quad \approx {\frac{1}{N_{s}M^{2}}{\sum\limits_{n = {- \infty}}^{\infty}{\sum\limits_{n = {- \infty}}^{\infty}{\left\lbrack {P\left( {{\frac{n}{\Delta} - \frac{\alpha}{\Delta\quad N_{s}}},{\frac{m}{\Delta} - \frac{\beta}{\Delta\quad N_{s}}}} \right)} \right\rbrack \times \quad\left\lbrack {H\left( {{\frac{n}{\Delta} - \frac{\alpha}{\Delta\quad N_{s}}},{\frac{m}{\Delta} - \frac{\beta}{\Delta\quad N_{s}}}} \right)} \right\rbrack \times \quad\left\lbrack {F\left( {{\frac{n}{M\quad\Delta} - \frac{\alpha}{M\quad\Delta\quad N_{s}}},{\frac{m}{M\quad\Delta} - \frac{\beta}{M\quad\Delta\quad N_{s}}}} \right)} \right\rbrack \times \quad\left\lbrack {\sum\limits_{k}{{\mathbb{e}}^{{- {\mathbb{i}}}\quad 2{\pi{({\frac{n}{\Delta} - \frac{\alpha}{\Delta\quad N_{s}}})}}\delta\quad x_{k}}{\mathbb{e}}^{{- {\mathbb{i}}}\quad 2\quad{\pi{({\frac{m}{\Delta} - \frac{\beta}{\Delta\quad N_{s}}})}}\quad\delta\quad y_{k}}}} \right\rbrack}}}}}}} & (51) \end{matrix}$

Assuming full fill factor rectangular pixels, P(u,v)=sinc(uΔ)sinc(uΔ). For a diffraction limited imaging H(u,v)=β¹(uλ/2N.A., uλ/2N.A.), where N.A. is the numerical aperture. β¹ is the first order B-spline.

Assuming that α and β are limited to [0, N_(s)−1], H(u,v) is nonvanishing in the sum of Eqn. (51) for: $\begin{matrix} {{n \leq {\frac{\alpha}{N_{s}} + \frac{2\quad\Delta\quad{N.A.}}{\lambda}}}{m \leq {\frac{\beta}{N_{s}} + \frac{2\quad\Delta\quad{N.A.}}{\lambda}}}} & (52) \end{matrix}$ This means that approximately $N_{sub} = {4\left( \frac{\Delta\quad{N.A.}}{\lambda} \right)^{2}}$ independent samples of F(u,v) are multiplexed in each Fourier sample. Ideally, N_(sub) copies of the image are sampled with different birefringent shifts to invert and reconstruct.

In the simplest case, it is assumed that just two shifted copies of the image in each subaperture. In this case, $\begin{matrix} {\left\lbrack {\sum\limits_{k}{{\mathbb{e}}^{{- {\mathbb{i}}}\quad 2\quad{\pi{({\frac{n}{\Delta} - \frac{\alpha}{\Delta\quad N_{s}}})}}\delta\quad x_{k}}{\mathbb{e}}^{{- {\mathbb{i}2}}\quad{\pi{({\frac{m}{\Delta} - \frac{\beta}{\Delta\quad N_{s}}})}}\delta\quad y_{k}}}} \right\rbrack = {\cos\left( {{2{\pi\left( {\frac{n}{\Delta} - \frac{\alpha}{\Delta\quad N_{s}}} \right)}\delta\quad x_{k}} + {2{\pi\left( {\frac{m}{\Delta} - \frac{\beta}{\Delta\quad N_{s}}} \right)}\delta\quad y_{k}}} \right)}} & (53) \end{matrix}$ The set of measurements over the subapertures of $F\left( {{\frac{n}{M\quad\Delta} - \frac{\alpha}{M\quad\Delta\quad N_{s}}},{\frac{m}{M\quad\Delta} - \frac{\beta}{M\quad\Delta\quad N_{s}}}} \right)$ is a DCT of Fourier coefficients over the range of n and m. Ideally inversion of this DCT requires $\begin{matrix} {{\delta_{x} = \frac{n^{\prime}\Delta}{\sqrt{N_{sub}}}}{\delta_{y} = \frac{m^{\prime}\Delta}{\sqrt{N_{sub}}}}} & (54) \end{matrix}$ where (n′,m′) is the subaperture index.

The pixel shift coding can be analyzed by similar Fourier methods. However, birefringent coding offers several improvements over pixel shift coding. In particular, birefringent shifts are coded by setting the thickness and orientation of a birefringent plate. Birefringent fan-out can be programmed at the tolerances required for this application. Exact coding of shifts has not been demonstrated to the same tolerance.

Also, birefringent shifts are self-registering. The challenge of shift coding is that the shift is object range dependent and alignment dependent. Subimage shifts in a birefringently coded system cause a global phase shift on the subimage Fourier transform. Given known constraints on the image (i.e., it is real and nonnegative), previous studies have shown that the image can be recovered from the magnitude of its Fourier transform. Thus, one algorithm can eliminate shift variance by considering |G_(α,β) ^(k)|. This system still contains the cosine transform of the modulus of F(u,v). In the birefringent coding case, the image is coded against its own reference.

6.3.3 Transmission Mask Coding

Another embodiment of the present invention is a method for birefringent shift coding. The transformation for coding with a focal plane mask is: g _(ij) =∫∫t(x,y)p(x−iΔ,y−jΔ)h(x−x′,y−y′)ƒ(mx′,my′)dxdydx′dy′  (55) The idea of coding with a mask is to select t(x,y) such that the linear transformation is well-conditioned for conventional or compressively sampled reconstruction. 6.3.3 Microoptic, Diffractive, and Interferometric Coding

While birefringent coding offers the simplest and least dispersive approach to optical prefiltering focal plane coding and compressive sampling encoding, micro-optical elements, such as lenslets, diffractive elements and prisms, can be used to reshape the impulse response in a coded manner for compressive or non-compressive imaging. Interferometers in the optical path, including Fabry-Perot, Fizeau and Mach-Zender devices, can also code the impulse response.

Another embodiment of the present invention is a method for optical prefilter encoding. The basic form of the transformation for optical prefilter encoding is g _(ij) ^(k) =∫∫p(x−iΔ,y−jΔ)h _(k)(x,x′,y,y′,λ)ƒ(mx′,my′,λ)dxdydx′dy′λ  (56) with the basic point being that the point spread function is not shift invariant and that it is non-redundantly coded in each subaperture. The possibility that the point spread function is dispersive (wavelength dependent) is also included.

Wavelength dispersion is both a challenge and an opportunity. In cases for which one is more interested in the achromatic image, wavelength dispersion can decrease signal to noise by binning light from similar spatial location in different detection channels. In cases where one is interested in multispectral or hyperspectral images, wavelength dispersion offers an opportunity to create a coded cross-spectral measurement system.

Temporal variations and adaptation in the optical prefilter is another possibility with micro-optical signal encoding. Using a spatial light modulator, such as a micromirror array, a liquid crystal or acoustic modulator, it is possible to vary h_(k)(x,x′,y,y′,λ)ƒ(mx′,my′,λ) as a function of time. This can be used to implement time domain compressive sampling or to match the PSF to the image signal.

6.4 Focal Plane Coding

Another embodiment of the present invention is a method for focal plane coding. A focal plane is an array of electronic detectors. In typical applications the array has a grid of rectangular pixels. Rectangular pixels are particularly appropriate for, for example, charge coupled device based detectors (CCDs). Other electronic detectors include active pixel CMOS VLSI detector arrays. In infrared systems, CMOS or CCD systems are used for the backend or front-end detector array.

Using CMOS detectors, microbolometer, or other detector arrays it is possible to shape both the spatial distribution of pixel sampling functions and the temporal read-out circuit for compressive sampling. The focal plane modulated image to measurement mapping is g _(ij) ^(k) =∫∫p _(k)(x−iΔ,y−jΔ,t)h(x−x′,y−y′)ƒ(mx′,my′)dxdydx′dy′  (57) The possibility of temporal variations is included in the pixel sampling function. This constitutes temporal variation in the read-out rate and read-out filter from different pixels rather than temporal changes in the spatial structure.

Generalized filtering in the temporal read-out circuit enables compressive sampling in time as well as space. From a spatial perspective, the lay-out of the pixel sampling structure achieves the same advantages as a transmission mask without masking. In addition, however, electronic interconnections and space-time filtering enable focal planes to implement non-convex, disjoint and multiscale pixel sampling functions that can be difficult to achieve with simple mask sampling.

FIG. 24 is a flowchart showing an exemplary method 2400 for spatially focal plane coding an optical signal using an imaging system, in accordance with an embodiment of the present invention.

In step 2410 of method 2400, the imaging system is created from a plurality of subimaging systems. Each subimaging system includes an array of electronic detectors. Each subimaging system is dispersed spatially with respect to a source of the optical signal according to a sampling function.

In step 2420, the optical signal is collected at each array of electronic detectors of the plurality of subimaging systems at a single time step.

In step 2430, the optical signal is transformed into a subimage at each subimaging system. The subimage includes at least one measurement from the array of electronic detectors of the subimaging system.

In step 2440, an image of the optical signal is calculated from the sampling function and each subimage, spatial location, pixel sampling function, and point spread function of each subimaging system of the plurality of subimaging systems. The sampling function is selected so that the number of measurements for the plurality of subimages is less than the number of estimated optical signal values that make up the image.

FIG. 25 is a flowchart showing an exemplary method 2500 for temporally focal plane coding an optical signal using an imaging system, in accordance with an embodiment of the present invention.

In step 2510 of method 2500, the imaging system is created from a plurality of subimaging systems. Each subimaging system includes an array of electronic detectors.

In step 2520, the optical signal is collected at each array of electronic detectors of the plurality of subimaging systems at a different time step according to a temporal sampling function.

In step 2530, the optical signal is transformed into a subimage at each subimaging system of the plurality of subimaging systems. The subimage included at least one measurement from an array of electronic detectors of each subimaging system.

In step 2540, an image of the optical signal is calculated from the temporal sampling function and each subimage, time step, pixel sampling function, and point spread function of each subimaging system of the plurality of subimaging systems. The temporal sampling function is selected so that the number of measurements for the plurality of subimages is less than the number of estimated optical signal values comprising the image.

FIG. 26 is a flowchart showing an exemplary method 2600 for spatially and temporally focal plane coding an optical signal using an imaging system, in accordance with an embodiment of the present invention.

In step 2610 of method 2600, the imaging system is created from a plurality of subimaging systems. Each subimaging system includes an array of electronic detectors. Each subimaging system is dispersed spatially with respect to a source of the optical signal according to a spatial sampling function.

In step 2620, the optical signal is collected at each array of electronic detectors of the plurality of subimaging systems at a different time step according to a temporal sampling function.

In step 2630, the optical signal is transformed into a subimage at each subimaging system of the plurality of subimaging systems. The subimage includes at least one measurement from an array of electronic detectors of the each subimaging system.

In step 2640, an image of the optical signal is calculated from the spatial sampling function, the temporal sampling function and each subimage, spatial location, time step, pixel sampling function, and point spread function of each subimaging system of the plurality of subimaging systems. The spatial sampling function and the temporal sampling function are selected so that the number of measurements for the plurality of subimages is less than the number of estimated optical signal values that make up the image.

7. Dynamic and Adaptive Coding Systems

It is useful in some cases to apply dynamic, rather than static, sampling strategies in compressive sampling systems. In such cases, micro-electro-optical mechanical devices or other dynamic spatial light modulators are used to adapt sampling filters and codes.

In accordance with an embodiment of the present invention, instructions configured to be executed by a processor to perform a method are stored on a computer-readable medium. The computer-readable medium can be a device that stores digital information. For example, a computer-readable medium includes a compact disc read-only memory (CD-ROM) as is known in the art for storing software. The computer-readable medium is accessed by a processor suitable for executing instructions configured to be executed. The terms “instructions configured to be executed” and “instructions to be executed” are meant to encompass any instructions that are ready to be executed in their present form (e.g., machine code) by a processor, or require further manipulation (e.g., compilation, decryption, or provided with an access code, etc.) to be ready to be executed by a processor.

Systems and methods in accordance with an embodiment of the present invention disclosed herein can advantageously reduce sensor cost, reduce sensor data load, reduce processing load, increase spatial sampling capacity and improve spatial abstraction.

For spectral bands at the edge of the visible spectrum, such as infrared, millimeter wave and ultraviolet ranges detector cost is a significant component of overall system cost. Compressive sampling enables lower system cost by reducing the detector count necessary for a given range and resolution specification.

In highly parallel sensor systems, such as imaging systems, read-out data loads may be overwhelming. Compression at the point of sampling may reduce these data loads and enable lower power, lower bandwidth sensor design.

Many sensor systems implement data compression in software immediately after sensing. Direct compression in sampling reduces or eliminates post sensor processing for data compression and analysis.

Sensor systems are limited by physical geometric constraints. Hyperspectral imagers, for example, require simultaneous sampling of myriad spatial and spectral channels. Efficient packing of sampling points requires mechanical scanning or large system apertures. Compressive sampling enables spatial and spectral sampling to be folded into smaller system volumes and shorter scanning times.

Sensor systems are based on naive isomorphisms between object and measurement spaces. The discrete abstractions of compressive sampling enable generalized models for the object space, including multidimensional object spaces and differences between the dimensionality of the measurement and object space.

8. Multiplex and Reflective Compressing Sampling

FIG. 27 is a flowchart showing an exemplary method 2700 for compressively sampling an optical signal using multiplex sampling, in accordance with an embodiment of the present invention.

In step 2710 of method 2700, group measurement of multiple optical signal components on single detectors is performed such that less than one measurement per signal component is taken.

In step 2720, probabilistic inference is used to produce a decompressed optical signal from the group measurement on a digital computer.

FIG. 28 is a flowchart showing an exemplary method 2800 for compressively sampling an optical signal using a reflective mask, in accordance with an embodiment of the present invention.

In step 2810 of method 2800, a reflective mask with a plurality of reflective elements and a plurality of non-reflective elements is created. The location of the plurality of reflective elements and the plurality of non-reflective elements is determined by a reflection function. The reflective mask includes but is not limited to a reflection mask or a coded aperture.

In step 2820, the spectrum of the optical signal is dispersed across the reflective mask.

In step 2830, signals reflected by the plurality of reflective elements are detected in a single time step at each sensor of a plurality of sensors dispersed spatially with respect to the reflective mask. Each sensor of the plurality of sensors produces a measurement resulting in a number of measurements for the single time step.

In step 2840, a number of estimated optical signal values is calculated from the number of measurements and the reflection function. The reflection function is selected so that the number of measurements is less than the number of estimated optical signal values.

In another embodiment of the present invention, the number of estimated optical signal values is calculated by multiplying the number of measurements by a pseudo-inverse of the reflection function. In another embodiment of the present invention, the number of estimated optical signal values is calculated by a linear constrained optimization procedure. In another embodiment of the present invention, the number of estimated optical signal values is calculated by a non-linear constrained optimization procedure. In another embodiment of the present invention, the reflective mask is adaptively or dynamically variable. In another embodiment of the present invention, the adaptively or dynamically variable reflective mask is based on electro-optic effects, liquid crystals, or micro mechanical devices. In another embodiment of the present invention, the reflective mask implements positive and respective negative elements of an encoding.

FIG. 29 is a flowchart showing an exemplary method 2900 for compressively sampling an optical signal using an optical component to encode multiplex measurements, in accordance with an embodiment of the present invention.

In step 2910 of method 2900, a mapping is created from the optical signal to a detector array by spatial and/or spectral dispersion.

In step 2920, signals transmitted are detected by a plurality of transmissive elements of the optical component at each sensor of a plurality of sensors of the detector array dispersed spatially with respect to the optical component. Each sensor of the plurality of sensors produces a measurement resulting in a number of measurements.

In step 2930, a number of estimated optical signal values is calculated from the number of measurements and a transmission function, wherein the transmission function is selected so that the number of measurements is less than the number of estimated optical signal values.

Another embodiment of the present invention is a spectrometer using compressive sampling. The spectrometer includes a plurality of optical components and a digital computer. The plurality of optical components measure multiple linear projections of a spectral signal. The digital computer performs decompressive inference on the multiple linear projections to produce a decompressed signal.

9. Focal Plane Coding for Compressive Imaging

Electronic focal planes for imaging applications typically consist of a planar array of detection elements. The detection elements map a continuous light field, ƒ(x,y), onto a discrete array of detected signal values g_(nm). Typically, this mapping is mathematically described as g _(nm)=∫ƒ(x′,y′)h(x−x′,y−y′)s(x−nΔ,y−nΔ)dx′dy′dxdy

where h(x,y) is the optical point spread function (PSF) and s(x,y) is the pixel sampling function. A is the pixel period. In many cases, g_(nm) is considered the “image” of ƒ(x,y).

According to various embodiments of the present invention, it is possible to encode h(x,y), s(x,y), or both h(x,y) and s(x,y) so as to enable estimation of ƒ(x,y) with greater resolution or fidelity than can be obtained without such encoding. It is specifically possible to estimate a greater number of pixels in ƒ(x,y) than the number of measurements in g_(nm). This process consists of decompressive signal inference, for example.

In various embodiments of the present invention, methods are used to encode h(x,y), s(x,y), or both h(x,y) and s(x,y) so as to enable decompressive inference. These methods may be used to increase the effective pixel count in a compressive imaging system. In the case of multicolor channel imaging, these methods may increase the number of effective resolution cells that can be estimated in the spectral data cube.

These methods include the programming of deliberate patterns in h(x,y), s(x,y), or both h(x,y) and s(x,y) and the use of nonlinear estimation algorithms to reconstruct ƒ(x,y) with greater resolution. In another embodiment of the present invention, h(x,y) may be dynamically or adaptively programmed so as to enable temporal encoding. This embodiment may increase the conditioning of signal estimation and reduce the otherwise required level of decompressive inference.

Coding in h(x,y) can enable decompressive inference by a number of mechanisms. In one embodiment, a high bandwidth but spatially dispersed PSF enables one point to register a pattern on multiple focal plane detectors. Whereas the position of an object point without PSF dispersion is ambiguous, with a dispersed PSF the position of the object can be accurately estimated to subpixel values. The combination of a dispersed PSF and nonlinear deconvolution enables high resolution estimation of an image from compressive projections.

In another embodiment, h(x,y) may be dynamically or adaptively programmed to shift the light field relative to the focal plane sampling or to apply multiple decodable PSFs based on adaptive or feedforward inference algorithms.

In another embodiment, h(x,y) may “fold” the light field on the focal plane such that multiple sections of the image are simultaneously captured by the same sensor pixels. The PSFs of different sections may vary in such a way that statistical inference enables disambiguation.

In another embodiment of the present invention, coding in s(x,y) enables decompressive inference producing structured projections for effective signal inference. Methods for signal inference may consist of linear remappings onto a representation basis or of nonlinear inference algorithms. Maximum likelihood algorithms, for example, can achieve effective decompressive inference.

Coding of either h(x,y) or s(x,y) can produce a “multiplex measurement” under which each measurement value g_(m) is a linear sum of object pixel values. h(x,y) is generally, but not absolutely, shift invariant as indicated in the object-measurement mapping value. Similarly, s(x,y) may not be shift invariant. Multiplex measurement is implemented in s(x,y) by analog or digital “fan-out” or interconnections, for example. Coding of s(x,y) may consist simply of geometrically shaped pixel functions to facilitate compressive measurement.

FIG. 30 is a schematic diagram showing a focal plane system 3000 for estimating an image 3010, in accordance with an embodiment of the present invention. System 3000 includes detector array 3020 and processor 3030. Detector array 3020 is encoded with a pixel sampling function. Detector array 3020 measures the image. Processor 3030 calculates an estimated image from the measured image and the pixel sampling function. Processor 3030 can include, but is not limited to, a computer, a microprocessor, or an application specific integrated circuit. The number of pixels in the estimated image is greater than the number of measurements in the measured image.

In another embodiment of system 3000, the detector array can be encoded by making analog interconnections of detector array elements, making digital interconnections of detector array elements, or geometrically shaping pixel functions of detector array elements. In another embodiment of system 3000, processor 3030 calculates an estimated image from the measured image and the pixel sampling function using decompressive inference. Decompressive inference can include using a maximum likelihood algorithm.

FIG. 31 is a schematic diagram showing a focal plane system 3100 for estimating an image 3110 that includes an optical component 3140, in accordance with an embodiment of the present invention. System 3100 includes detector array 3120 and processor 3130, in addition to optical component 3140. Optical component 3140 creates a dispersed PSF and encodes the image with the dispersed PSF. Detector array 3120 measures the encoded image. Processor 3130 calculates an estimated image from the measured image and the dispersed PSF. The number of pixels in the estimated image is greater than the number of measurements in the measured image. Optical component 3140 can include, but is not limited to, a diffractive, a refractive, or an interferometric optical element. The processor can calculate the estimated image using an estimation-maximization algorithm, for example.

In another embodiment of system 3100, a dynamic optical modulator is used to dynamically change the dispersed PSF. The dynamic optical modulator can include, but is not limited to, a micromechanical device, a liquid crystal modulator, a micromirror, a piezoelectronic device, or an electro-optic device.

In another embodiment of system 3100, detector array 3120 is encoded with a pixel sampling function. Processor 3130 calculates the estimated image from the dispersed PSF, the pixel sampling function, and the measured image. Processor 3130 calculates the estimated image using decompressive inference, for example.

FIG. 32 is a flowchart showing a method 3200 for estimating an image using an encodement provided by an optical component, in accordance with an embodiment of the present invention.

In step 3210 of method 3200, a dispersed PSF is created and the image is encoded with the dispersed PSF using the optical component. The optical component can be, but is not limited to, a diffractive, a refractive, or an interferometric optical element.

In step 3220, the encoded image is measured using a detector array.

In step 3230, an estimated image is calculated from the dispersed PSF and the measured image using a processor. The number of pixels in the estimated image is greater than the number of measurements in the measured image.

In another embodiment of method 3200, the dispersed PSF enables a point to register a pattern on two or more elements of the detector array. In another embodiment of method 3200, the estimated image is calculated using an estimation-maximization algorithm. The estimation-maximization algorithm includes nonlinear deconvolution or a Lucy-Richardson deconvolution, for example.

In another embodiment of method 3200, the dispersed PSF is dynamically or adaptively changed using a dynamic optical modulator. The dynamic optical modulator can be, but is not limited to, a micromechanical device, a liquid crystal modulator, a micromirror, a piezoelectronic device, or an electro-optic device.

In another embodiment of method 3200, two or more sections of the encoded image are simultaneously captured by a pixel sensor of the detector array.

FIG. 33 is a flowchart showing a method 3300 for estimating an image using an encodement provided by a detector array, in accordance with an embodiment of the present invention.

In step 3310 of method 3300, the detector array is encoded with a pixel sampling function. The method by which the detector array is encoded can include, but is not limited to, making analog interconnections of detector array elements, making digital interconnections of detector array elements, or geometrically shaping pixel functions of detector array elements.

In step 3320, the image is measured using the detector array.

In step 3330, an estimated image is calculated from the pixel sampling function and the measured image using a processor. The number of pixels in the estimated image is greater than the number of measurements in the measured image. The estimated image is calculated using decompressive inference, for example. Decompressive inference can use, for example, a maximum likelihood algorithm.

FIG. 34 is a flowchart showing a method 3400 for estimating an image using an encodement provided by an optical component and an encodement provided by a detector array, in accordance with an embodiment of the present invention.

In step 3410 of method 3400, a dispersed PSF is created and the image is encoded with the dispersed PSF using the optical component. The optical component can be, but is not limited to, a diffractive, a refractive, or an interferometric optical element.

In step 3420 of method 3400, the detector array is encoded with a pixel sampling function. The method by which the detector array is encoded can include, but is not limited to, making analog interconnections of detector array elements, making digital interconnections of detector array elements, and geometrically shaping pixel functions of detector array elements.

In step 3430, the image is measured using the detector array.

In step 3440, an estimated image is calculated from the dispersed PSF, the pixel sampling function, and the measured image using a processor. The number of pixels in the estimated image is greater than the number of measurements in the measured image.

In another embodiment of method 3400, the dispersed PSF enables a point to register a pattern on two or more elements of the detector array.

In another embodiment of method 3400, the dispersed PSF is dynamically or adaptively changed using a dynamic optical modulator. The dynamic optical modulator can be, but is not limited to, a micromechanical device, a liquid crystal modulator, a micromirror, a piezoelectronic device, or an electro-optic device.

In another embodiment of method 3400, the estimated image is calculated using decompressive inference.

Another embodiment of the present invention is a method for encoding h(x,y) by placing optical elements on a system aperture or in an optical system to produce a multiple lobe PSF. Imaging with such a system is illustrated in FIGS. 35-38.

FIG. 35 is 90 by 140 portion of an exemplary 500 by 500 image 3500 of a random point cloud, in accordance with an embodiment of the present invention. The random point cloud is sparse and compressible.

FIG. 36 is an exemplary 32 by 32 dispersed PSF 3600 created by an optical component of a focal plane imager, in accordance with an embodiment of the present invention. PSF 3600 is created by putting diffraction gratings on the aperture of a focal plane imager, for example. A single grating harmonic produces a delta function response in the image plane corresponding to a single point.

FIG. 37 is an exemplary 120 by 120 measured image 3700 of the 500 by 500 point cloud, partially shown in FIG. 35, using a focal plane imager, in accordance with an embodiment of the present invention. Measured image 3700 is produced by 4 times undersampling from PSF 3600, shown in FIG. 36. Measured image 3700, shown in FIG. 37, is reduced by a factor of 16 relative to the true object size.

FIG. 38 is a 90×140 portion of an exemplary estimated image 3800 of the 500 by 500 point cloud, partially shown in FIG. 35, using a focal plane imager, in accordance with an embodiment of the present invention. Estimated image 3800 is a high resolution image of the original object with excellent fidelity.

In accordance with an embodiment of the present invention, instructions configured to be executed by a processor to perform a method are stored on a computer-readable medium. The computer-readable medium can be a device that stores digital information. For example, a computer-readable medium includes a compact disc read-only memory (CD-ROM) as is known in the art for storing software. The computer-readable medium is accessed by a processor suitable for executing instructions configured to be executed. The terms “instructions configured to be executed” and “instructions to be executed” are meant to encompass any instructions that are ready to be executed in their present form (e.g., machine code) by a processor, or require further manipulation (e.g., compilation, decryption, or provided with an access code, etc.) to be ready to be executed by a processor.

The foregoing disclosure of the preferred embodiments of the present invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many variations and modifications of the embodiments described herein will be apparent to one of ordinary skill in the art in light of the above disclosure. The scope of the invention is to be defined only by the claims appended hereto, and by their equivalents.

Further, in describing representative embodiments of the present invention, the specification may have presented the method and/or process of the present invention as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. As one of ordinary skill in the art would appreciate, other sequences of steps may be possible. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. In addition, the claims directed to the method and/or process of the present invention should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the present invention. 

1. A method for estimating an image, comprising: encoding the image with a dispersed point spread function created by an optical component; measuring the encoded image using a detector array; and calculating an estimated image from the dispersed point spread function and the measured image using a processor, wherein a number of pixels in the estimated image is greater than a number of measurements in the measured image.
 2. The method of claim 1, wherein the optical component comprises one of a diffractive, refractive, and interferometric optical element.
 3. The method of claim 1, wherein the dispersed point spread function enables a point to register a pattern on two or more elements of the detector array.
 4. The method of claim 1, wherein calculating the estimated image comprises an estimation-maximization algorithm.
 5. The method of claim 4, wherein the estimation-maximization algorithm comprises nonlinear deconvolution.
 6. The method of claim 4, wherein the estimation-maximization algorithm comprises Lucy-Richardson deconvolution.
 7. The method of claim 1, further comprising dynamically changing the dispersed point spread function using a dynamic optical modulator.
 8. The method of claim 7, wherein the dynamic optical modulator comprises one of a micromechanical device, a liquid crystal modulator, a micromirror, a piezoelectronic device, and an electro-optic device.
 9. The method of claim 1, wherein two or more sections of the encoded image are simultaneously captured by a pixel sensor of the detector array.
 10. A method for estimating an image, comprising: encoding a detector array with a pixel sampling function; measuring the image using the detector array; and calculating an estimated image from the pixel sampling function and the measured image using a processor, wherein a number of pixels in the estimated image is greater than a number of measurements in the measured image.
 11. The method of claim 10, wherein encoding the detector array comprises one of making analog interconnections of detector array elements, making digital interconnections of detector array elements, and geometrically shaping pixel functions of detector array elements.
 12. The method of claim 10, wherein calculating an estimated image comprises using decompressive inference.
 13. The method of claim 12, wherein decompressive inference comprises a maximum likelihood algorithm.
 14. A method for estimating an image, comprising: encoding the image with a dispersed point spread function created by an optical component; encoding a detector array with a pixel sampling function; measuring the encoded image using the detector array; and calculating an estimated image from the dispersed point spread function, the pixel sampling function, and the measured image, wherein a number of pixels in the estimated image is greater than a number of measurements in the measured image.
 15. The method of claim 14, wherein the optical component comprises one of a diffractive, refractive, and interferometric optical element.
 16. The method of claim 14, wherein the dispersed point spread function enables a point to register a pattern on two or more elements of the detector array.
 17. The method of claim 14, further comprising dynamically changing the dispersed point spread function using a dynamic optical modulator.
 18. The method of claim 17, wherein the dynamic optical modulator comprises one of a micromechanical device, a liquid crystal modulator, a micromirror, a piezoelectronic device, and an electro-optic device.
 19. The method of claim 14, wherein encoding the detector array comprises one of making analog interconnections of detector array elements, making digital interconnections of detector array elements, and geometrically shaping pixel functions of detector array elements.
 20. The method of claim 14, wherein calculating an estimated image comprises using decompressive inference.
 21. A focal plane system for estimating an image, comprising: an optical component that creates a dispersed point spread function and encodes the image with the dispersed point spread function; a detector array that measures the encoded image; and a processor that calculates an estimated image from the dispersed point spread function and the measured image, wherein a number of pixels in the estimated image is greater than a number of measurements in the measured image.
 22. The system of claim 21, wherein the optical component comprises one of a diffractive, refractive, and interferometric optical element.
 23. The system of claim 21, wherein the processor calculates the estimated image using an estimation-maximization algorithm.
 24. The system of claim 21, further comprising a dynamic optical modulator for dynamically changing the dispersed point spread function.
 25. The system of claim 24, wherein the dynamic optical modulator comprises one of a micromechanical device, a liquid crystal modulator, a micromirror, a piezoelectronic device, and an electro-optic device.
 26. A focal plane system for estimating an image, comprising: a detector array that is encoded with a pixel sampling function and measures the image; and a processor that calculates an estimated image from the pixel sampling function and the measured image, wherein a number of pixels in the estimated image is greater than a number of measurements in the measured image.
 27. The system of claim 26, wherein the detector array is encoded by one of making analog interconnections of detector array elements, making digital interconnections of detector array elements, and geometrically shaping pixel functions of detector array elements.
 28. The system of claim 26, wherein the processor calculates the estimated image using decompressive inference.
 29. The system of claim 28, wherein the decompressive inference comprises a maximum likelihood algorithm.
 30. A focal plane system for estimating an image, comprising: an optical component that creates a dispersed point spread function and encodes the image with the dispersed point spread function; a detector array that is encoded with a pixel sampling function and measures the encoded image; and a processor that calculates an estimated image from the dispersed point spread function, the pixel sampling function, and the measured image, wherein a number of pixels in the estimated image is greater than a number of measurements in the measured image.
 31. The system of claim 30, wherein the processor calculates the estimated image using decompressive inference. 